Python Software Engineer (Product Engineering Team)

About Lineate
Lineate is a US-based international software development company with over two decades of experience.

From Intelligent Document Processing(IDP) and Agentic RAG systems to scalable cloud architectures, we turn complex ideas into real, measurable results.

We deliver AI-driven custom solutions for FinTech, HealthTech, AdTech, and beyond, empowering businesses to grow smarter, faster, and more efficiently.

Our Expertise Falls Into Three Main Categories

• Building Custom AI Solutions: Deploying high-impact, AI-enabled technology utilizing IDP, Agentic RAG.
• Cloud and Data Infrastructure: Optimizing business operations with our data management and cloud computing solutions.
• Team Augmentation: Providing specialized experts in FinTech, AdTech, and HealthTech to integrate seamlessly and accelerate project timelines.
• Our goal is not just to build technology, but to build the future operating model for our clients.

Role Overview
We are looking for a
senior Data Engineer / ClickHouse Engineer
with deep hands-on expertise in ClickHouse and AWS. The role focuses on building and optimizing high-performance data infrastructure for
large-scale time-series and real-time workloads
in a lakehouse environment.

Key Responsibilities

• Design, implement, and optimize ClickHouse for large-scale analytical and time-series workloads.
• Build and maintain data ingestion and transformation pipelines.
• Optimize ClickHouse schemas, partitioning, materialized views, aggregations, and queries.
• Support real-time and near-real-time query serving, ensuring performance and reliability.
• Work with a lakehouse architecture, using Amazon S3 as the source of truth.
• Integrate ClickHouse with Snowflake, Databricks, and other components of the data ecosystem.
• Support ClickHouse infrastructure and BYOC deployments on AWS.
• Troubleshoot data ingestion, storage, scalability, and performance issues.
• Own technical decisions and clearly communicate solutions and trade-offs to stakeholders.

Requirements
Must-have

• 6+ years of Data Engineering experience.
• Strong production-level ClickHouse experience.
• Deep knowledge of ClickHouse data modeling, table engines, partitioning, ordering, materialized views, aggregations, and query optimization.
• Strong hands-on AWS experience, particularly with Amazon S3.
• Experience with lakehouse-style architectures and object storage as the primary data layer.
• Experience with large-scale time-series data and real-time or near-real-time analytics.
• Strong performance-tuning and troubleshooting skills.
• Ability to work independently and take technical ownership.
• Strong communication and stakeholder-management skills.

Nice-to-have

• ClickHouse certification
• Experience with BYOC on AWS.
• Experience with Snowflake and/or Databricks integrations.
• Background working with high-volume financial, transactional, telemetry, or similar datasets.
• Experience delivering complex data solutions in enterprise environments.

We offer

• Freedom to Develop - equal opportunity to learn and grow professionally
• Clear career and professional path, strong performance management system
• Social Benefits Package
• Equipment
• English lesson
• Gym membership
• All the advantages of working in an international IT company
• Flexible vacation time
• Fun and inclusive in-person and digital events

Lineate is proud to be an Equal Opportunity Employer - We do not hire on the basis of race, colour, religion, creed, gender, national origin, citizenship, age, disability, veteran status, marital status, pregnancy, parental status, sex, gender expression or identity, sexual orientation, or any other basis protected by Georgian legislation. All employment is decided based on qualifications, merit, and business ne
ed. Learn more about Lineate at our website: www.lineate
.com

Are you looking for a great opportunity to expand your skills and knowledge? Want to join a multinational company with interesting projects and learn from our professionals? If you are ready to start your career in IT, just show us your potential, and we will give you the experience!

Responsibilities

• Learning under the supervision of an experienced professionals
• Practice on educational and life projects
• Spend from 30 to 40 hours per week for 6 months on developing yourself in your chosen field

Requirements

• Basic knowledge of test design and test theory
• Basic Knowledge in Java
• Understanding of OOP
• Basic understanding of Git
• Understanding of Software development lifecycle
• English: Intermediate level or higher (spoken and written)
• Education: Bachelor degree (preferably Computer Science, Information Technologies, or related discipline)

Nice to have

• Basic knowledge of relational DB concepts and SQL
• Basic knowledge of at least one QA Automation framework/library
• Basic knowledge of Selenium
• Basic knowledge of Understanding of client-server communication
• Basic knowledge of Design Patterns.

We offer

• The duration of the internship is 6 months;
• 6 to 8 working hours a day;
• Compensation for the period of study;
• Mentoring from professional specialists, development plan, monitoring of learning progress, technical assessment upon completion of training;
• The best students will have the opportunity for further employment in the company;
• Opportunity to participate in all activities of the company;
• We provide all the necessary equipment;
• Access to training platforms.

About Us
Grid Dynamics (NASDAQ: GDYN) is a leading provider of technology consulting, platform and product engineering, AI, and advanced analytics services. Fusing technical vision with business acumen, we solve the most pressing technical challenges and enable positive business outcomes for enterprise companies undergoing business transformation. A key differentiator for Grid Dynamics is our 8 years of experience and leadership in enterprise AI, supported by profound expertise and ongoing investment in data, analytics, cloud & DevOps, application modernization, and customer experience. Founded in 2006, Grid Dynamics is headquartered in Silicon Valley with offices across the Americas, Europe, and India.

• Provectus is an AWS Premier Partner and an Anthropic Strategic Partner, working at the frontier of applied AI. We help enterprises turn Claude, agentic systems, and their own data into measurable business outcomes — through bespoke applications, managed services, and advisory engagements. With offices in North America, LATAM, and EMEA, we partner with clients worldwide.
• Our work centers on two verticals — Financial Services & Insurance and Healthcare & Life Sciences — where we deploy five pre-built AI Blueprints: Submission Flow, Portfolio Lens, Asset Flow, Revenue Flow, and Evidence Lens. Each Blueprint rebuilds a critical business process front to back, shipped from working code and tuned to a client's specific book, regulators, and operating posture.
• Our team holds 100+ AWS certifications, is Claude Code certified, and co-delivers Anthropic's Agentic SDLC program, Cowork Activation, and AI Blueprint engagements.
• Where this role sits

You will work in a small, senior pod alongside an FDX; our delivery arc is Sprint → Enable → Realize:

• Forward Deployed Executive (FDX) owns the commercial relationship and the business outcome. Works alongside the client's leadership or C-suite level to move the client's KPIs — revenue growth, cost reduction, risk reduction
• Forward Deployed Engineer (FDE) embeds with a client to change how that client operates. You own the method; nobody hands you a ticket. You map the client workflow as it actually happens, identify the business problem underneath it, build a working AI solution, present to the client in the language of outcomes, and transfer it. Provectus maintains industry Blueprints — working systems that have already shipped for a client in the same industry — so you begin from running code and tuning it to this client's specific book, regulators, and operating posture. You will be measured on whether the Business Unit's number moved, not on hours or scope delivered.

What You’ll Do:
Take the seat

• Sit with the client and the Forward Deployed Executive at the start of an engagement. Learn the function from inside, not from a requirements doc, and redesign the function from first principles.
• Reach working fluency in a new domain — insurance underwriting, healthcare revenue cycle, asset flow.

Build

• Design and ship production GenAI systems into the customer’s environment (cloud-native data, LLM-based, and agentic AI solutions). Implement and optimize RAG systems for production use cases
• Build the evaluation harness before you build the feature. Define what working means, instrument it, and let the evals drive the design.
• Write production code across the stack — AI, backend services, data pipelines. We choose tools to fit the customer.
• Take systems to production on AWS (GCP or Azure where the customer requires it): containerised, CI/CD, automated testing, monitoring, and maintainable after we leave. Hand the system over to the client.
• Start from the blueprint, and feed the blueprint. What you learn in the field becomes the baseline the next engagement starts from.
• Lead architecture reviews, produce technical design documents, and contribute to standards. Mentor engineers and share knowledge across the team.

Own the outcome.

• Work in a pair with a FDX who carries the Business Unit’s KPIs. Your work is measured against the same number.
• Own the technical direction of technical proposals and scoping. Drive adoption. Change management is part of the engineering job here.
• Be credible with the customer’s engineers and their executives.
• Shape what we commit to before we commit to it.

What You’ll Bring:
Mindset

• Proactive and self-directed; identify problems before they're handed to you
• Comfort with ambiguity and ownership. Engagements start underspecified by design. Closing that gap is the job
• B2+ English, comfortable collaborating across distributed, multicultural teams

Client Engagement

• You are willing to spend time understanding and doing someone else’s job on the client's side before you write a line of code
• Credible with senior stakeholders — you can hold a redesign conversation with a BU head and a scoping conversation with a CTO, presenting outcomes to them
• You can produce a scoped, phased delivery plan with clear deliverables, dependencies, and risks — and estimate what it will cost to build and to run

Technical depth

• 7+ years building and running production systems.
• Solid AI/ML foundations. You understand what the models do well enough to reason about failure modes
• Designed and shipped to production LLM applications and agentic workflows — not demos, not POCs, not notebooks
• Agentic orchestration: multi-step workflows, graph-based orchestration, tool use, state management, and recovery from partial failure
• Experience with LLM APIs (Anthropic, AWS Bedrock, or OpenAI) and agent frameworks.
• Experience building and optimizing RAG systems in production
• Strong engineering fundamentals — dropped into an unfamiliar codebase or language, you’re productive. Python and/or TypeScript proficiency; depth matters more than stack.
• Experience in making and defending architectural trade-off decisions
• Hands-on AWS production depth: Bedrock, Bedrock AgentCore, Lambda, ECS, S3, SQS, ECR, or similar. GCP or Azure is a plus
• Cloud-native delivery: containers, ECS or Kubernetes, IaC, and CI/CD applied to AI pipelines
• You evaluate. You have built or owned an eval suite for a non-deterministic system, and you can explain what you measured, how you produced ground truth, and what gated a release
• Model and agent monitoring, drift detection
• Cost and latency discipline: model tiering, caching, and the ability to say what a workload costs to run before it runs
• Hands-on production experience with the Claude ecosystem — Claude Code, CLAUDE.md, hooks, skills files. Spec-driven development — writing the intent, constraints, and acceptance criteria before you let an agent build — is a strong plus
• MCP: you can say why an agent would prefer it to a REST integration. Having authored a server is a plus

Nice to have:

• Prior experience as a founder, CTO, or engineering leader who has chosen to return to individual contribution
• Experience in one of the industries: financial services, insurance, healthcare
• Consulting, professional services, or other embedded customer-facing delivery
• A2A: you can explain agent-to-agent interoperability
• AWS and Claude Code Certifications
• CI/CD pipeline experience (GitHub Actions, GitLab CI)
• Experience in an additional language (Go, TypeScript, or Rust)
• Experience with Apache Spark, Apache Airflow, Kafkа

What We Offer:

• The chance to shape how leading enterprises across LATAM, Europe, and North America adopt AI, from strategy through first deployment
• A forward-deployed model working in small, senior teams alongside Principal Architects and Forward Deployed Engineers
• A growing AI delivery practice where you help build the tooling and frameworks, not just use them
• Remote-friendly culture
• Internal training programs with full support for Claude, AWS, and other professional certifications, conference attendance
• Career growth; we actively develop our engineers
• Access to the latest AI tools and premium subscriptions
• Long-term B2B collaboration
• Private medical insurance or a budget for your medical needs
• Paid sick leave, vacation, and public holidays
• Equipment and all the tech you need for comfortable, productive work

How we hire:

• Intro conversation. The role, your background and aspirations, tech questions.
• Technical interview with live engineering sessions. Real problems, your own editor, you may use an LLM assistant
• HR Interview. Soft skills and expectations
• HM interview. Tech questions; a live engineering session is also possible

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

• Provectus is an AWS Premier Partner and an Anthropic Strategic Partner, working at the frontier of applied AI. We help enterprises turn Claude, agentic systems, and their own data into measurable business outcomes — through bespoke applications, managed services, and advisory engagements. With offices in North America, LATAM, and EMEA, we partner with clients worldwide.
• Our work centers on two verticals — Financial Services & Insurance and Healthcare & Life Sciences — where we deploy five pre-built AI Blueprints: Submission Flow, Portfolio Lens, Asset Flow, Revenue Flow, and Evidence Lens. Each Blueprint rebuilds a critical business process front to back, shipped from working code and tuned to a client's specific book, regulators, and operating posture.
• Our team holds 100+ AWS certifications, is Claude Code certified, and co-delivers Anthropic's Agentic SDLC program, Cowork Activation, and AI Blueprint engagements

Where this role sits
You will work in a small, senior pod alongside an FDE and an FDX,

• Forward Deployed Executives (FDX) own the commercial relationship and the business outcome. Works alongside the client's leadership or C-suite level to move the client's KPIs.
• Forward Deployed Engineers (FDE) embed with a client, map the client workflow, identify the business problem underneath it, design and build a working AI solution, present to the client, and transfer the knowledge to the client's team. Owns technical direction of the whole solution.
• Senior AI Engineer. When an FDE comes back from the client with the business problem, you will help turn that into an agentic system that runs in production and will be responsible for evaluation, observability, and guardrails. You'll have real ownership of components and of the technical decisions inside them.

Requirements:
Mindset

• Proactive and self-directed; you push for clarity rather than waiting for a ticket
• Excellent communication and problem-solving skills
• Comfort with ambiguity and ownership.
• B2+ English, comfortable collaborating across distributed, multicultural teams.

Technical depth

• 5+ years in software or ML engineering, with production systems you were accountable for.
• Solid AI/ML foundations. You understand what the models do well enough to reason about failure modes.
• Shipped to production LLM applications and agentic workflows — not demos, not POCs, not notebooks.
• Agentic orchestration: multi-step workflows, graph-based orchestration, tool use, state management, and recovery from partial failure
• Experience with LLM APIs (Anthropic, AWS Bedrock, or OpenAI) and agent frameworks.
• Experience building and optimizing RAG systems in production.
• Strong engineering fundamentals. Full-stack mindset, comfortable across AI, backend development, and cloud infrastructure. Python and/or TypeScript proficiency; depth matters more than stack. Dropped into an unfamiliar codebase, you're productive.
• Hands-on AWS in production: Bedrock, Bedrock AgentCore, Lambda, ECS, S3, SQS, ECR, or similar. GCP or Azure is a plus.
• Cloud-native delivery: containers, ECS or Kubernetes, IaC, and CI/CD applied to AI pipelines.
• You evaluate. You have built or owned an eval suite for a non-deterministic system, and you can explain what you measured, how you produced ground truth, and what gated a release.
• Model and agent monitoring, drift detection.
• Cost and latency discipline: model tiering, caching, and the ability to say what a workload costs to run before it runs.
• Hands-on production experience with the Claude ecosystem — Claude Code, CLAUDE.md, hooks, skills files. Spec-driven development — writing the intent, constraints, and acceptance criteria before you let an agent build — is a strong plus.
• MCP: you can say why an agent would prefer it to a REST integration. Having authored a server is a plus.

Nice to Have:

• Experience in one of the industries: financial services, insurance, healthcare.
• Consulting, professional services, or other embedded customer-facing delivery.
• AWS and Claude Code Certifications
• A2A: you can explain agent-to-agent interoperability
• CI/CD pipeline experience (GitHub Actions, GitLab CI)
• Practical experience with one or more use cases from the following: NLP, LLMs, and Recommendation engines.
• Experience in an additional language (Go, TypeScript, or Rust).
• Experience with Apache Spark, Apache Airflow, Kafkа

Responsibilities:

• Work in a pair with an FDE and an FDX.
• Build and ship production GenAI systems into the customer’s environment (cloud-native data, LLM-based, and agentic AI solutions).
• Build and optimize RAG systems for production use cases
• Build the evaluation harness before you build the feature.
• Write production code across the stack — AI, backend services, data pipelines. We choose tools to fit the customer.
• Integrate AI components into backend services and RESTful APIs
• Take systems to production on AWS (GCP or Azure where the customer requires it): containerised, CI/CD. Implement LLMOps and AgentOps practices: agent tracing, prompt and version management, cost and latency monitoring, regression testing, drift detection
• Start from the blueprint, contribute to enablement and handover: clear documentation, runbooks, and pairing with the client engineers who will inherit the system. Feed reusable components and lessons back into the Provectus Blueprints
• Participate in technical discussions and architectural decisions
• Conduct model evaluation, improve failure modes you find, optimize model performance, efficiency, and reliability
• Mentor junior and mid-level AI engineers, conduct code reviews and share knowledge across the team through documentation, presentations, and workshops.

What We Offer:

• The chance to shape how leading enterprises across LATAM, Europe, and North America adopt AI, from strategy through first deployment
• A forward-deployed model working in small, senior teams alongside FDE and FDX
• A growing AI delivery practice where you help build the tooling and frameworks, not just use them
• Remote-friendly culture
• Internal training programs with full support for Claude, AWS, and other professional certifications, conference attendance
• Career growth; we actively develop our engineers
• Access to the latest AI tools and premium subscriptions
• Long-term B2B collaboration
• Private medical insurance or a budget for your medical needs
• Paid sick leave, vacation, and public holidays
• Equipment and all the tech you need for comfortable, productive work

How we hire:

• Intro conversation. The role, your background and aspirations, tech questions.
• Technical interview with live engineering sessions. Real problems, your own editor, you may use an LLM assistant
• HR Interview. Soft skills and expectations
• HM interview. Tech questions; a live engineering session is also possible

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.