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Tech Lead

LatentBridge
Pune, IND
Full-time
Senior · 8+ years experience
Onsite
Discovered 1 months ago
PythonLangChainLangGraphLlamaIndexSemantic KernelMCP

Full Job Posting

  • 8–12+ years of overall software engineering experience
  • 3+ years of strong hands-on experience in AI/ML, GenAI or related AI engineering
  • Strong hands-on Python development
  • Strong recent hands-on experience building GenAI/LLM-based applications
  • Strong experience with LLMs, prompt engineering, structured outputs and tool/function calling
  • Hands-on experience with RAG, embeddings, vector databases, document processing, chunking, retrieval and reranking
  • Hands-on experience with AI agents and agent orchestration, including multi-step workflows, tool-using agents, memory/state management and human-in-the-loop patterns
  • Experience with LangChain, LangGraph, LlamaIndex, Semantic Kernel or similar frameworks
  • Good understanding of MCP and emerging standards for connecting AI agents with enterprise systems and tools
  • Experience with backend development using FastAPI, Flask, Django or similar frameworks
  • Strong understanding of REST APIs, microservices and distributed application architecture
  • Experience integrating enterprise applications, databases and third-party APIs
  • Strong coding, debugging, troubleshooting and performance optimisation skills
  • Experience owning solution architecture and technical design for enterprise applications
  • Experience taking solutions from discovery/prototype through development and production deployment
  • Hands-on exposure to at least one major cloud platform: Azure, AWS or GCP
  • Experience with Docker, Kubernetes, CI/CD, cloud-native application deployment, API management, logging/monitoring and identity/access management
  • SQL and relational databases; NoSQL databases; vector databases
  • Data ingestion and transformation pipelines; API-based integration; event-driven/asynchronous processing
  • Understanding of enterprise authentication/authorization, data privacy, PII handling and enterprise security requirements
  • Understanding of secure AI architecture, data protection, prompt/input security, AI guardrails, logging, auditability, monitoring, evaluation, regression testing, scalability and cost management
  • Experience leading technical teams while continuing to contribute to development
  • Strong client-facing and communication skills
  • Experience working in Agile delivery environments
  • Ability to move from Client Problem → Solution Architecture → Technical Design → Team Guidance → Hands-on Coding → Code Review → Deployment → Production Support Good-to-Have Skills
  • Experience with Azure AI Foundry, AWS Bedrock, Google Vertex AI or similar enterprise AI platforms
  • Experience with OpenAI, Azure OpenAI, Anthropic Claude, Google Gemini or equivalent models
  • Traditional ML/ML engineering knowledge
  • LLM evaluation frameworks
  • AI guardrails and responsible AI
  • LLM observability and tracing
  • Model and prompt evaluation
  • Token, latency and cost optimisation
  • Experience building enterprise AI accelerators or reusable AI platforms
  • Experience with multi-agent or agentic AI solutions
  • Experience modernising existing enterprise applications using AI
  • Microsoft Fabric or enterprise data platforms
  • BFSI, financial services or other regulated enterprise environments
  • AI security and responsible AI practices
  • Experience supporting technical proposals, estimations and solution presentations
  • Experience mentoring engineers and building engineering standards or reusable frameworks
  • Git-based development, branching, pull requests and code reviews
  • Experience with API management, secrets/configuration management and production troubleshooting Key Responsibilities
  • Understand business requirements and translate them into the right technical solution
  • Own overall architecture and technical design of AI, GenAI and agentic AI solutions
  • Define application architecture, AI/LLM components, APIs, integrations, data flows, security and deployment approach
  • Evaluate technology and model options based on business need, cost, performance, security and scalability
  • Create architecture diagrams, technical design documents, API specifications and implementation guidelines
  • Identify technical risks and drive practical solutions
  • Actively contribute to coding throughout the project
  • Build critical modules, prototypes, reusable components and integrations
  • Develop and integrate LLM applications, RAG pipelines, AI agents and APIs
  • Support complex coding, integration and performance issues
  • Conduct code reviews and ensure good engineering practices
  • Improve code quality, performance, security and maintainability
  • Lead and guide AI/ML engineers, backend developers and other technical team members
  • Break solutions into technical work packages and guide implementation
  • Support estimation, sprint planning and technical task allocation
  • Track technical progress and address dependencies/blockers
  • Mentor team members and improve technical capabilities
  • Review designs and code before higher environments
  • Ensure technical quality throughout the project
  • Work closely with Project Managers, Business Analysts, Solution Architects, QA and DevOps teams
  • Own technical delivery and ensure alignment with agreed architecture
  • Participate in client discovery and technical workshops
  • Understand client landscape, integrations, data, security and infrastructure constraints
  • Explain architecture and technical decisions to technical and business stakeholders
  • Present solution architecture and technical options during client reviews
  • Support pre-sales with technical solutioning, estimates, architecture and feasibility assessments
  • Handle technical questions and challenges during client discussions
  • Take AI solutions beyond prototype into production, including security, governance, evaluation, monitoring, scalability, performance and cost management something around - Anthropic Claude certifications (particularly CCAF for architects), Microsoft AI-103, AWS Certified Generative AI Developer – Professional. Education / Qualification
  • Bachelor's or Master's degree in Computer Science, Engineering, Information Technology or a related discipline
  • Equivalent strong hands-on engineering experience may also be considered

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