AI Lead (Python)

Location: Hyderabad, India. Based at the KnackLabs headquarters, with occasional travel to client locations for workshops and reviews. This role does not involve extended onsite deployments.

About the Role

You will work as an AI Architect who designs the systems behind our client engagements: AI agents, RAG systems, automation platforms, and the conventional backend systems around them.

This is a hands-on design role, not a slideware role. You will scope architectures with clients, make the hard technical decisions, defend them in review, and stay accountable for how the systems perform in production.

You will work directly with clients. Everyone at KnackLabs does. You will sit in design discussions with client engineering teams, present architecture decisions to technical and business stakeholders, and answer for the choices you make.

A full KnackLabs engineering team in Hyderabad builds with you. You own the technical design and the quality of what ships.

What you'll own

1. Architecture - Design AI agents, RAG systems, integrations, and the scalable backend systems around them, for multiple client engagements.
2. Technical scoping - Work directly with clients to turn a business problem into a system design, with clear trade-offs and clear reasons.
3. Scale and reliability - Make sure what we build handles real load: data stores, queues, caching, horizontal scaling, and fault tolerance.
4. Design reviews - Review designs and builds across engagements. Set the technical bar and hold it.
5. Evaluation strategy - Define how we measure accuracy, safety, latency, and cost for the AI systems we ship.
6. Guiding engineers - Raise the level of the engineers building with you, through reviews and direct pairing.
7. Feedback to the platform - Feed what you learn across engagements back into our platform and internal tools.

What we are looking for

1. Around 7 or more years of software engineering experience, including direct work with customers on design or delivery.
2. Full-stack development experience with strength in backend technologies.
3. Experience designing and building scalable applications. You understand how large-scale distributed systems work: data partitioning, queues, caching, horizontal scaling, and fault tolerance.
4. At least 2 years of strong, hands-on AI experience with large language models in production.
5. You build with AI coding tools like Claude Code or Codex as your default way of working. You understand Claude Skills, have written skills yourself, use them actively, and have contributed to them.
6. Hands-on experience building retrieval-augmented generation (RAG) systems: chunking, embeddings, vector databases, retrieval, and reranking.
7. Hands-on experience building AI agents.
8. Strong programming skills in Python. Working knowledge of TypeScript or JavaScript.
9. Experience with at least one cloud platform (AWS, Azure, or GCP).
10. Clear communication. You can explain an architecture decision to an engineer and to a business leader, and defend it under questioning.
11. High ownership and comfort with ambiguity. You can take an unclear problem and turn it into a design.

Nice to have

1. Experience building evaluations to measure accuracy, safety, latency, and cost.
2. Experience with observability and tracing tools such as LangSmith or Braintrust.
3. Experience with on-premises or private cloud (VPC) deployments.
4. Experience deploying AI systems in regulated industries such as insurance, banking, or the public sector.
5. Experience with data engineering and pipelines.
6. A history of side projects, open source contributions, or products you shipped end-to-end.
7. Experience working at a consulting or professional services firm in a client-facing delivery role.

Stack and tools

1. Languages: Python and TypeScript.
2. Models: Claude and other frontier or open-source models, chosen to fit the customer.
3. AI patterns: RAG, agents, prompt engineering, skills, and evaluations.
4. Vector and retrieval: vector databases and retrieval pipelines.
5. Cloud: AWS, Azure, or GCP, on public or private cloud.
6. Integration: REST APIs and enterprise system connectors.

About the Role

We are seeking a hands-on Tech Lead to design, build, and integrate AI-driven systems that automate and enhance real-world business workflows. This is a high-impact role for someone who enjoys full-stack ownership — from backend AI architecture to frontend user experiences — and can align engineering decisions with measurable product outcomes.

You will begin as a strong individual contributor, independently architecting and deploying AI-powered solutions. As the product portfolio scales, you will lead a distributed team across India and Australia, acting as a System Integrator to align engineering, data, and AI contributions into cohesive production systems.

Example Project

Design and deploy a multi-agent AI system to automate critical stages of a company’s sales cycle, including:

• Generating client proposals using historical SharePoint data and CRM insights
• Summarizing meeting transcripts
• Drafting follow-up communications
• Feeding structured insights into dashboards and workflow tools

The solution will combine RAG pipelines, LLM reasoning, and React-based interfaces to deliver measurable productivity gains.

Key Responsibilities

• Architect and implement AI workflows using LLMs, vector databases, and automation frameworks
• Act as a System Integrator, coordinating deliverables across distributed engineering and AI teams
• Develop frontend interfaces using React/JavaScript to enable seamless human-AI collaboration
• Design APIs and microservices integrating AI systems with enterprise platforms (SharePoint, Teams, Databricks, Azure)
• Drive architecture decisions balancing scalability, performance, and security
• Collaborate with product managers, clients, and data teams to translate business use cases into production-ready systems
• Mentor junior engineers and evolve into a broader leadership role as the team grows

Ideal Candidate Profile

Experience Requirements

• 5+ years in full-stack development (Python backend + React/JavaScript frontend)
• Strong experience in API and microservice integration
• 2+ years leading technical teams and coordinating distributed engineering efforts
• 1+ year of hands-on AI project experience (LLMs, Transformers, LangChain, OpenAI/Azure AI frameworks)
• Prior experience in B2B SaaS environments, particularly in AI, automation, or enterprise productivity solutions

Technical Expertise

• Designing and implementing AI workflows including RAG pipelines, vector databases, and prompt orchestration
• Ensuring backend and AI systems are scalable, reliable, observable, and secure
• Familiarity with enterprise integrations (SharePoint, Teams, Databricks, Azure)
• Experience building production-grade AI systems within enterprise SaaS ecosystems

Job Description – AI Engineer (End-to-End Development & Deployment)

Role Summary

We are looking for an AI Engineer with hands-on experience in designing, developing, deploying, and maintaining Generative/Agentic AI solutions in production. The ideal candidate should have end-to-end ownership of AI applications, from development to deployment, monitoring, and optimization.

Key Responsibilities

●        Design, build, and deploy Generative/Agentic AI solutions.

●        Develop applications using LLMs, RAG, AI agents, and vector databases.

●        Build scalable APIs and integrate AI solutions with enterprise applications.

●        Implement CI/CD pipelines, containerization, and MLOps best practices.

●        Monitor, optimize, and maintain production AI systems.

●        Collaborate with cross-functional teams to deliver business-driven AI solutions.

Required Skills

●       Strong programming skills in Python.

●       Experience with vector databases (e.g., Pinecone, FAISS, ChromaDB) and graph memory systems

●       Knowledge of atleast one agent development framework: Google ADK (preferred), LangChain/LangGraph/LlamaIndex, CrewAI

●       Experience with LLMs, RAG, GenAI, AgenticAI Agents

●       Hands-on experience with FastAPI, and REST APIs.

●       Knowledge of Docker, Kubernetes, Git, CI/CD.

●       Experience with AWS, Azure, or GCP. 

●       Experience with security compliance, monitoring and observability tools such as AWS CloudWatch, Azure Monitor, Google Cloud Monitoring.

What We Are Looking For

CLOUDSUFI is seeking a senior, hands-on AI Platform Architect to design and build production-grade platforms for generative AI, agentic systems, data-intensive applications, and analytical workflows. This is a builder-architect role. The successful candidate will define architecture, make technology decisions, develop reference implementations, review critical code and designs, and guide engineering teams from prototypes to secure, scalable production systems. We are looking for a builder-architect with strong engineering judgement and practical delivery experience. The right candidate can define platform direction, evaluate trade-offs, validate ideas through implementation, and guide systems into production. They should be equally comfortable discussing distributed architecture, reviewing code, diagnosing workflow failures, designing evaluation systems, and mentoring engineering teams.

Key Responsibilities-

AI and Agentic Platform Architecture

• Design platforms for single-agent and multi-agent systems supporting planning, reasoning, tool use, memory, delegation, validation, and human approval.

• Define orchestration patterns for deterministic, dynamic, event-driven, and long-running AI workflows.

• Establish clear boundaries between LLM reasoning, application logic, quantitative computation, rules, and human decision-making.

• Evaluate and adopt agent frameworks, model providers, tools, and orchestration technologies based on reliability, flexibility, performance, and cost. Knowledge and Data Systems

• Architect RAG pipelines, document-processing systems, vector search, hybrid retrieval, knowledge graphs, and semantic data layers.

• Integrate structured and unstructured enterprise data from APIs, databases, files, streams, and external platforms.

• Design reusable workflows for research, data collection, transformation, analysis, modelling, validation, and reporting.

• Establish data lineage, provenance, metadata, access controls, freshness, and quality standards. Evaluation, Observability and Governance

• Build evaluation frameworks for accuracy, relevance, groundedness, task completion, tool use, safety, latency, and cost.

• Enable systematic experimentation across models, prompts, agents, tools, retrieval strategies, and orchestration patterns.

• Implement versioning and lifecycle management for prompts, agents, workflows, datasets, knowledge bases, evaluations, and model configurations.

• Establish tracing, monitoring, auditability, guardrails, approval workflows, and production quality diagnostics.

Cloud and Platform Engineering

• Define cloud-native architectures using microservices, APIs, event-driven systems, queues, schedulers, and distributed processing.

• Lead Kubernetes-based deployment, containerisation, CI/CD, Infrastructure as Code, environment management, and release automation.

• Design for horizontal scalability, fault tolerance, resilience, security, data privacy, and high availability.

• Optimise model usage, infrastructure, storage, retrieval, and compute for performance, latency, and cost.

Technical Leadership

• Translate product and business requirements into clear technical designs and implementation plans.

• Build prototypes and reference implementations for high-risk or foundational platform capabilities.

• Review architecture, code, interfaces, data models, infrastructure, and operational readiness.

• Define engineering standards and reusable patterns across AI, backend, data, and platform teams.

• Mentor senior engineers and support teams in resolving complex technical and production issues.

Required Skills and Experience

• 10+ years of experience in software architecture, platform engineering, distributed systems, data platforms, or AI systems.

• Strong hands-on experience designing and building production-grade AI or data-intensive platforms.

• Deep understanding of LLM applications, tool calling, structured outputs, RAG, embeddings, memory, and agent orchestration.

• Strong experience with cloud platforms, Kubernetes, containers, microservices, APIs, event driven architecture, CI/CD, and Infrastructure as Code.

• Experience with relational, document, graph, vector, and distributed data systems.

• Practical experience implementing AI evaluation, experimentation, tracing, monitoring, guardrails, and lifecycle management.

• Strong understanding of security, identity, access control, secrets management, data protection, and production reliability.

• Ability to move effectively between architecture, code, infrastructure, debugging, and technical delivery.

Good to Have

• Experience building enterprise AI copilots, autonomous workflows, research platforms, or analytical systems.

• Experience with knowledge graphs, hybrid search, model gateways, tool gateways, or agent marketplaces.

• Familiarity with LLMOps, MLOps, model serving, feature stores, model registries, and distributed compute.

• Experience supporting real-time and batch data processing at scale.

• Experience comparing and operating multiple commercial and open-source models.

• Prior experience in consulting, client-facing architecture, or complex enterprise platform delivery. 

Location: Jaipur (Work From Office)

Employment Type: Full-Time

We're looking for a GenAI Engineer (LLM Engineer) to build scalable AI-powered SaaS applications using Large Language Models (LLMs). You'll develop intelligent AI workflows, integrate LLMs into production systems, and build secure, high-performance AI solutions.

Key Responsibilities

• Integrate LLM APIs (OpenAI, Claude, Hugging Face) into production applications.
• Design and optimize RAG pipelines and prompt engineering workflows.
• Build and manage Vector Databases (Pinecone, Weaviate, pgvector).
• Optimize AI performance, latency, and operational cost.
• Ensure secure, scalable AI architecture.
• Collaborate with Product and Engineering teams to deliver AI-powered features.

Requirements

• 3+ years of backend development using Python, Go, or Node.js.
• Hands-on experience with LLMs, LangChain or LlamaIndex.
• Strong understanding of RAG, Prompt Engineering, and Vector Databases.
• Experience with AWS, GCP, or Azure.
• Knowledge of APIs, Microservices, and AI application development.

Preferred: Experience in SaaS/FinTech, LLMOps, or Model Fine-tuning.

Education: B.Tech, BCA, or equivalent technical qualification.

Apply Now

Application Form: https://zfrmz.com/pAKb2ynfomIsuNwRfRbV?utm_source=cutshort

Key Responsibilities:

·      Architectural Leadership: Design and lead the development of robust, scalable AI architectures, ensuring high performance, reliability, and security.

·      Applied Mathematics & Statistics: Apply statistical analysis, numerical computation, and mathematical modeling to derive insights from large-scale data and optimize model performance.

·      Deep Learning Development: Design, train, and deploy advanced Deep Learning (DL) models.

·      Technical Mentorship: Mentor engineering teams on best practices for AI/ML, coding standards, and architectural design.

·      Model Optimization: Optimize models for speed, efficiency, and accuracy using techniques like pruning, quantization, or GPU acceleration.

·      Strategy & Innovation: Evaluate and select appropriate AI frameworks, tools, and platforms, staying abreast of cutting-edge research and industry trends.

Qualifications:

Required:

·      Education: Master's or PhD in Computer Science, Applied Mathematics, Statistics, Physics, or a related quantitative field.

·      Experience: 10+ years of experience in software development, with at least 3-5 years in a Applied Mathematics and Deep learning.

·      AI/ML Expertise: Proven experience designing and deploying deep learning models in production using frameworks.

·      Mathematics/Statistics: Strong proficiency in linear algebra, calculus, probability, and statistical methods.

·      Programming Skills: Expert-level coding skills in Python (NumPy, Pandas, Scikit-learn) and experience with languages like Java or C++.

Key Competencies:

• Strategic mindset with deep operational awareness.
• Excellent communication and stakeholder management skills.
• Ability to simplify complex technical concepts for executive reporting.
• Strong leadership, people development, and cross-functional influencing skills.

Bias for action and a relentless focus on continuous improvement.

Senior Generative AI Engineer

Employment Type: Permanent with VDart Digital

Work Location: Marathalli, Bengaluru

Job Description

We are seeking a highly skilled Senior Generative AI Engineer with strong expertise in designing, developing, and deploying enterprise-scale AI solutions using Large Language Models (LLMs) and modern Generative AI frameworks. The ideal candidate should have hands-on production experience building scalable GenAI applications, AI agents, autonomous workflows, and Retrieval-Augmented Generation (RAG) systems in cloud-native environments.

This role requires deep technical expertise in LLM orchestration, AI application architecture, prompt engineering, vector databases, MLOps, and production deployment of AI systems. Candidates should have proven experience delivering real-world AI solutions in enterprise environments with strong exposure to cloud platforms and DevOps practices.

Key Responsibilities

• Design, build, and deploy enterprise-grade Generative AI applications using Large Language Models (LLMs).
• Develop intelligent AI agents and autonomous workflows using frameworks such as LangChain, CrewAI, LangGraph, AutoGen, or similar agentic AI frameworks.
• Implement and optimize Retrieval-Augmented Generation (RAG) pipelines using vector databases and semantic search technologies.
• Work extensively on prompt engineering, tool calling, memory management, agent orchestration, and multi-agent systems.
• Integrate and manage LLMs such as OpenAI, Azure OpenAI, Claude, Gemini, Llama, Mistral, or similar foundation models.
• Develop scalable AI services and APIs using Python and FastAPI.
• Build production-ready AI solutions with high availability, scalability, monitoring, and observability.
• Deploy and manage AI applications in cloud-native environments using Docker and Kubernetes.
• Collaborate with Data Science, ML Engineering, and DevOps teams to operationalize AI solutions.
• Implement CI/CD pipelines and automated deployment processes for AI workloads.
• Monitor model performance, latency, reliability, and operational efficiency in production environments.
• Ensure AI solutions follow enterprise security, governance, and responsible AI standards.
• Evaluate and adopt emerging Generative AI tools, frameworks, and models.

Required Skills

Generative AI & LLM Expertise

• Strong hands-on experience with Generative AI and Large Language Models (LLMs).
• Production-level experience building and deploying GenAI applications.
• Expertise in LangChain, CrewAI, LangGraph, AutoGen, or similar frameworks.
• Experience with AI agents, autonomous workflows, and multi-agent architectures.
• Strong understanding of prompt engineering, embeddings, model evaluation, and LLM orchestration.
• Experience integrating OpenAI, Azure OpenAI, Claude, Gemini, Llama, Mistral, or similar models.

RAG & Vector Databases

• Strong experience implementing RAG pipelines and semantic retrieval systems.
• Experience with vector databases such as Pinecone, Weaviate, ChromaDB, FAISS, or Milvus.
• Understanding of chunking strategies, embeddings, indexing, reranking, and retrieval optimization.

Python & AI Development

• Strong proficiency in Python.
• Experience with FastAPI for AI service and API development.
• Experience with AI/ML libraries and data processing tools such as Pandas and NumPy.

Cloud & Production Deployment

• Mandatory production experience on at least one cloud platform:
• Microsoft Azure
• Experience deploying scalable AI applications in enterprise production environments.
• Hands-on experience with Docker, Kubernetes, Jenkins, Terraform, and CI/CD pipelines.
• Strong understanding of MLOps, AI deployment lifecycle, monitoring, and observability.

Engineering & Operational Excellence

• Strong understanding of software engineering best practices.
• Experience with Git, version control, automated testing, and release management.
• Experience building secure, scalable, and high-performance AI solutions.
• Ability to troubleshoot production AI systems and optimize performance.

Preferred Skills

• Experience with AI observability and evaluation frameworks.
• Exposure to fine-tuning, PEFT, LoRA, or model optimization techniques.
• Experience with enterprise AI governance and responsible AI practices.
• Knowledge of distributed AI systems and scalable inference architectures.
• Familiarity with AI security and compliance standards.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or a related field.
• 3–8 years of overall software engineering experience.
• Minimum 3+ years of hands-on experience in Generative AI and LLM-based application development,
• Proven track record of delivering enterprise-scale AI solutions in production environments.
• Strong communication and stakeholder management skills.

Senior AI Engineer

Code Generation, Agent Architecture & LLM Systems

📍 Mumbai (On-site) | Full-time | 5+ years

About the Role:

Unico Connect is an AI-first technology partner that builds custom mobile, web, and AI products for clients across multiple geographies.

We are hiring a Senior AI Engineer for a dedicated client engagement focused on building an AI-powered application builder platform - a product where users describe software in plain English and the system generates, previews, and iteratively refines working code.

The mandatory requirement for this role is hands-on production experience shipping LLM-powered systems with agent architectures, with experience in code generation or developer tooling contexts a strong advantage.

The role is product-focused and deeply hands-on. You will own everything between the user's prompt and correct code landing in the project: the agentic loop, code generation pipeline, context management, evaluation suite, and model cost strategy.

You will work alongside the Senior MLOps Engineer who operationalises the infrastructure around your system, and collaborate closely with backend, frontend, and DevOps engineers.

Responsibilities:

Agent Architecture

Design and own the agentic loop for the platform - request interpretation, planning, tool-calling sequence (read file, edit file, run build, search code, install package), and stop conditions.

Make and revisit architectural decisions on single-agent vs. multi-agent designs, including planner/executor splits and dedicated build-repair sub-agents.

Code Generation Pipeline

Own the end-to-end generation flow: task classification, context gathering, planning, targeted edits, verification, and commit.

Implement diff/search-replace-based file editing with fuzzy matching and fallback strategies.

Enforce scope discipline so the agent makes minimal diffs and does not modify code it was not asked to touch.

Self-Repair Loop

Build and tune the automated repair loop that pipes compiler, lint, build, and runtime errors back to the model with retry budgets and model escalation.

This loop is the primary quality lever - the difference between 60-70% and 90%+ build success rates.

Context Management

Build file-relevance retrieval so the agent sees the right files, not the whole codebase: dependency graphs, AST/tree-sitter-based chunking, embeddings, recency signals, and hybrid retrieval.

Implement conversation summarisation and memory for long sessions, and address long-project degradation through codebase summaries and periodic consistency passes.

Own token budgeting and prompt caching strategy.

Prompt Engineering as a Discipline

Own the system prompt and per-task prompt variants (new feature, bug fix, styling change).

Maintain few-shot examples and enforce coding conventions, stack rules, and prohibited behaviours such as no hardcoded secrets and no whole-file rewrites.

Version prompts like code with changelogs and rollback capability.

Evaluation and Quality Measurement

Design and own the evaluation suite: representative test prompts run on every prompt and model change, scored on build success rate, instruction adherence, and output quality including LLM-as-judge and visual/screenshot checks where relevant.

Define regression gates that block quality-degrading changes from shipping.

Treat evals the way engineers treat automated testing: versioned, automated, and tracked over time.

This responsibility is non-negotiable at this level.

Model Strategy and Cost

Design model routing - cheap and fast models for classification and small edits, frontier models for complex generation.

Drive cost optimisation through prompt caching, diff-based edits over full-file rewrites, and tighter context selection.

Track cost per agent run and tokens per task; evaluate new model releases against the eval suite and lead migrations when results justify it.

Safety and Reliability of Agent Behaviour

Defend against prompt injection from user content and fetched web content.

Ensure secrets never appear in generated client code.

Define what the agent's tools may and may not do in collaboration with the platform team.

Contribute to output moderation and abuse-pattern awareness.

Mentorship and Engineering Standards

Run code reviews, define engineering conventions for AI work, and raise the engineering bar across the AI team.

Work closely with the Senior MLOps Engineer on handoff of eval design, prompt configurations, and model routing logic.

Requirements:

Hands-on Production Ownership of LLM-Powered Systems with Agent Architectures (Mandatory)

Must have personally shipped and operated at least one complex production AI system - agentic, multi-step, or code generation - with end-to-end ownership of architecture, evaluation, and cost.

POCs, internal demos, and tutorial-grade work do not qualify.

5+ Years of Professional Software or AI Engineering Experience

With at least 3 years focused on LLM applications, AI engineering, or production AI systems.

Candidates with strong backend backgrounds and a clear, substantive pivot into LLM systems qualify.

Strong Python Proficiency and Service Development

Production-grade Python with FastAPI or equivalent: type hints, async patterns, streaming responses, testing, and packaging.

Not notebook-only.

Depth Across LLM APIs and Agent Systems

Production experience with at least two of OpenAI, Anthropic Claude, Google Gemini, or open-weight models (vLLM, Ollama, Together).

Production experience with at least one agent framework (LangGraph, CrewAI, AutoGen, LlamaIndex Agents) or hand-rolled equivalent.

Hands-on with tool calling, structured outputs, and multi-step reasoning.

Demonstrated, Systematic Evaluation Practice - Non-Negotiable

Must have built evaluation harnesses that gate production releases, not ad-hoc testing.

Hands-on with at least one of LangSmith, Langfuse, Promptfoo, Ragas, or DeepEval.

Candidates with no systematic answer to evaluation should not be considered at senior level regardless of other strengths.

Cost Discipline for Production AI

Track record of measurable cost optimisation on production AI features.

Able to speak in specifics: cost per request, savings achieved through caching or model routing, context reduction decisions.

AWS Working Knowledge

Hands-on with EC2, S3, IAM, and Docker.

Comfort with CI/CD workflows and deploying AI services.

Awareness of LLM Security Failure Modes

Familiar with prompt injection patterns, understands that system prompt rules alone are insufficient, and has experience with output validation and content safety in production.

Nice to Have

• Experience with AST/tree-sitter tooling, diff-based editing systems, or compiler-adjacent work
• MCP server authoring
• Open-source AI contributions
• Published technical writing on LLM systems
• Multi-modal model experience
• Fine-tuning exposure (LoRA, QLoRA, PEFT)

POSITION OVERVIEW

We are seeking an experienced Senior Data Scientist & Generative AI Specialist on a contractual basis to support a premier Germany-based chemical manufacturing enterprise. In this role, you will lead the end-to-end design, development, and deployment of production-grade GenAI applications, multi-modal LLM workflows, and advanced retrieval platforms tailored to complex industrial and enterprise data ecosystems.

Working closely with cross-functional global teams, you will build robust backend microservices, implement state- of-the-art RAG/GraphRAG architectures, and leverage cloud-native AI infrastructure (Azure, Vector DBs, Knowledge Graphs) to drive operational efficiency and data-driven innovation.

KEY RESPONSIBILITIES

• GenAI & LLM System Engineering: Design, build, and deploy production-grade multi-modal GenAI applications processing text, structured technical documentation, images, and telemetry data.
• Advanced RAG & Graph Architecture: Implement cutting-edge Retrieval-Augmented Generation (RAG) and GraphRAG pipelines using document parsing frameworks, custom embeddings, vector databases, and knowledge graphs to capture complex domain relationships.
• Scalable Backend Development: Architect high-throughput, low-latency microservice APIs using Python, FastAPI, and Flask, leveraging asynchronous programming (asyncio) and strict type validation (Pydantic) for long-running LLM processes.
• Agentic Systems & Azure Ecosystem: Build autonomous agent systems using modern frameworks (MCP, A2A) and orchestrate enterprise workflows across the Microsoft Azure AI ecosystem (Azure AI Foundry, AI Search, Document Intelligence, Databricks).
• Model Optimization & Evaluation: Execute systematic LLM fine-tuning, prompt optimization, and rigorous evaluation frameworks to assess AI output accuracy, reliability, and business impact against industrial requirements.
• Data Layer Management: Architect and maintain enterprise database layers combining SQL (PostgreSQL) for structured transactional data with specialized vector search engines and graph stores.
• Rapid Prototyping: Utilize AI-assisted development tools (Copilot, Claude Code) to accelerate delivery timelines and rapidly build functional UI prototypes for client feedback.

TECHNICAL QUALIFICATIONS

Core Development & Backend:

• Python Mastery: Deep expertise in writing clean, production-ready Python using asynchronous programming (asyncio), strict type-hinting (Pydantic), and automated testing patterns.

• Backend Microservices: Hands-on experience building microservices with FastAPI and Flask structured to handle asynchronous, long-running AI background tasks.

• Database Engineering: Strong command of PostgreSQL, relational schema design, vector indexing, and knowledge graph paradigms.

Machine Learning & AI Infrastructure:

• Model Expertise: Hands-on experience with leading multi-modal LLM architectures (OpenAI, Anthropic, Google) and domain-specific AI workflows.

• Retrieval & Parsing: Proven track record with document extraction frameworks, embedding models, vector search engines, and GraphRAG architectures.

• Cloud Infrastructure: Strong proficiency with Azure AI infrastructure (Foundry, Databricks, AI Search, Document Intelligence).

• Agentic Frameworks: Practical experience with open-source agent protocols (MCP, A2A), parameter-efficient fine-tuning (PEFT/LoRA), and model evaluation methodology.

CONTRACT & REMOTE REQUIREMENTS

• Contract Engagement: Contractual structure tailored to project milestones and deliverables.

• 100% Remote Setup: Fully equipped home office with high-speed, secure internet infrastructure.

• Timezone Overlap: Guaranteed 4-hour daily overlap with Central European Time (CET/CEST - Germany) to ensure smooth collaboration with enterprise stakeholders.

• Communication: Fluent professional English communication skills (written and spoken) for asynchronous and real-time technical coordination. 

Skill Set

Large language,Artificial Intelligence,Machine Learning

- 4–7 years of experience in software engineering/AI roles

- Strong programming skills in Python or TypeScript (Java/Go is a plus)

- Hands-on experience with LLMs, RAG pipelines, and AI frameworks

- Experience building APIs and working with distributed systems

- Familiarity with Kubernetes, Docker, and CI/CD pipelines

- Experience with cloud platforms (AWS/Azure/GCP)

Excellent communication