{"id":1227489,"url":"https://alion.io/job/latentbridge-technical-lead","title":"Technical Lead","company":{"id":3800380,"name":"LatentBridge","domain":"latentbridge.com","url":"https://alion.io/company/latentbridge","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Backend","role_family":"Backend","seniority":"lead","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":3000000,"max":5000000,"currency":"INR","period":"year","gross":true,"usd_annual":52410},"salary_estimate":null,"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"AI Agents","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Django","optional":false},{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"FastAPI","optional":false},{"name":"Flask","optional":false},{"name":"Function Calling","optional":false},{"name":"GCP","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Reranking","optional":false},{"name":"Rest API","optional":false},{"name":"Semantic Kernel","optional":false},{"name":"SQL","optional":false},{"name":"Structured Outputs","optional":false},{"name":"Anthropic","optional":true},{"name":"AWS Bedrock","optional":true},{"name":"Claude","optional":true},{"name":"Configuration Management","optional":true},{"name":"Gemini","optional":true},{"name":"Git","optional":true},{"name":"Microsoft Fabric","optional":true},{"name":"OpenAI","optional":true},{"name":"Vertex AI","optional":true}],"status":"live","first_seen_at":"2026-09-24T07:04:49Z","employer_posted_date":null,"last_verified_at":"2026-09-24T07:04:49Z","board_verified":false,"closed_at":null,"days_open":7,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":7},"description":"Role : GenAI / AI Engineering\n\nRole Focus : Hands-on technical leadership across GenAI, LLM applications, RAG, AI agents, backend engineering and enterprise cloud architecture.\n\nExperience : 8 - 12+ years overall software engineering experience, including 3+ years of strong hands-on AI/ML, GenAI or related AI engineering experience.\n\nRole Overview : \n\n- Translate client and business problems into practical technical solutions and scalable solution architecture.\n\n- Own the architecture and technical design of AI, GenAI and agentic AI solutions while remaining hands-on with development.\n\n- Move solutions from discovery and prototype through development, production deployment and production support.\n\n- Lead and guide engineers while contributing to coding, debugging, code reviews, performance optimisation and technical problem solving.\n\n- Work closely with clients and internal stakeholders on discovery, technical workshops, architecture decisions, estimates and solution presentations.\n\nCore Experience & Engineering Requirements : \n\n- 8 - 12+ years of overall software engineering experience.\n\n- 3+ years of strong hands-on experience in AI/ML, GenAI or related AI engineering.\n\n- Strong hands-on Python development.\n\n- Strong recent hands-on experience building GenAI/LLM-based applications.\n\n- Strong experience with LLMs, prompt engineering, structured outputs and tool/function calling.\n\n- Strong coding, debugging, troubleshooting and performance optimisation skills.\n\n- Experience owning solution architecture and technical design for enterprise applications.\n\n- Experience taking solutions from discovery/prototype through development and production deployment.\n\nGenAI, LLM & Agentic AI : \n\n- Hands-on experience with RAG, embeddings, vector databases, document processing, chunking, retrieval and reranking.\n\n- 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.\n\n- Experience with LangChain, LangGraph, LlamaIndex, Semantic Kernel or similar frameworks.\n\n- Good understanding of MCP and emerging standards for connecting AI agents with enterprise systems and tools.\n\n- Understanding of secure AI architecture, data protection, prompt/input security, AI guardrails, logging, auditability, monitoring, evaluation, regression testing, scalability and cost management.\n\nBackend, Cloud & Distributed Systems : \n\n- Experience with backend development using FastAPI, Flask, Django or similar frameworks.\n\n- Strong understanding of REST APIs, microservices and distributed application architecture.\n\n- Experience integrating enterprise applications, databases and third-party APIs.\n\n- Hands-on exposure to at least one major cloud platform : Azure, AWS or GCP.\n\n- Experience with Docker, Kubernetes, CI/CD, cloud-native application deployment, API management, logging/monitoring and identity/access management.\n\n- SQL and relational databases; NoSQL databases; vector databases.\n\n- Data ingestion and transformation pipelines; API-based integration; event-driven/asynchronous processing.\n\n- Understanding of enterprise authentication/authorization, data privacy, PII handling and enterprise security requirements.\n\nTechnical Leadership & Delivery : \n\n- Experience leading technical teams while continuing to contribute to development.\n\n- Ability to move from Client Problem - Solution Architecture - Technical Design - Team Guidance - Hands-on Coding - Code Review - Deployment - Production Support.\n\n- Break solutions into technical work packages and guide implementation.\n\n- Support estimation, sprint planning and technical task allocation.\n\n- Track technical progress and address dependencies/blockers.\n\n- Mentor team members and improve technical capabilities.\n\n- Review designs and code before higher environments and ensure technical quality throughout the project.\n\n- Work closely with Project Managers, Business Analysts, Solution Architects, QA and DevOps teams.\n\n- Work effectively in Agile delivery environments.\n\nPeople & Collaboration Skills : \n\n- Strong stakeholder management skills with the ability to build trust, align diverse teams and communicate clearly across technical and business audiences.\n\n- Strong mentoring, coaching and conflict-resolution skills, with the ability to give constructive feedback and create a collaborative engineering culture.\n\nClient-Facing & Pre-Sales Responsibilities : \n\n- Participate in client discovery and technical workshops.\n\n- Understand client landscape, integrations, data, security and infrastructure constraints.\n\n- Explain architecture and technical decisions to technical and business stakeholders.\n\n- Present solution architecture and technical options during client reviews.\n\n- Support pre-sales with technical solutioning, estimates, architecture and feasibility assessments.\n\n- Handle technical questions and challenges during client discussions.\n\nKey Responsibilities : \n\n- Understand business requirements and translate them into the right technical solution.\n\n- Own overall architecture and technical design of AI, GenAI and agentic AI solutions.\n\n- Define application architecture, AI/LLM components, APIs, integrations, data flows, security and deployment approach.\n\n- Evaluate technology and model options based on business need, cost, performance, security and scalability.\n\n- Create architecture diagrams, technical design documents, API specifications and implementation guidelines.\n\n- Identify technical risks and drive practical solutions.\n\n- Actively contribute to coding throughout the project.\n\n- Build critical modules, prototypes, reusable components and integrations.\n\n- Develop and integrate LLM applications, RAG pipelines, AI agents and APIs.\n\n- Support complex coding, integration and performance issues.\n\n- Conduct code reviews and ensure good engineering practices.\n\n- Improve code quality, performance, security and maintainability.\n\n- Lead and guide AI/ML engineers, backend developers and other technical team members.\n\n- Take AI solutions beyond prototype into production, including security, governance, evaluation, monitoring, scalability, performance and cost management.\n\nGood-to-Have Skills : \n\n- Experience with Azure AI Foundry, AWS Bedrock, Google Vertex AI or similar enterprise AI platforms.\n\n- Experience with OpenAI, Azure OpenAI, Anthropic Claude, Google Gemini or equivalent models.\n\n- Traditional ML/ML engineering knowledge.\n\n- LLM evaluation frameworks; AI guardrails and responsible AI; LLM observability and tracing.\n\n- Model and prompt evaluation.\n\n- Token, latency and cost optimisation.\n\n- Experience building enterprise AI accelerators or reusable AI platforms.\n\n- Experience with multi-agent or agentic AI solutions.\n\n- Experience modernising existing enterprise applications using AI.\n\n- Microsoft Fabric or enterprise data platforms.\n\n- BFSI, financial services or other regulated enterprise environments.\n\n- AI security and responsible AI practices.\n\n- Experience supporting technical proposals, estimations and solution presentations.\n\n- Experience mentoring engineers and building engineering standards or reusable frameworks.\n\n- Git-based development, branching, pull requests and code reviews.\n\n- Experience with API management, secrets/configuration management and production troubleshooting.\n\nPreferred Certifications & Qualifications : \n\nCertifications / Credentials : \n\n- Anthropic Claude certifications, particularly CCAF for architects.\n\n- Microsoft AI-103.\n\n- AWS Certified Generative AI Developer - Professional.\n\nEducation / Qualification : \n\n- Bachelor's or Master's degree in Computer Science, Engineering, Information Technology or a related discipline.\n\n- Equivalent strong hands-on engineering experience may also be considered.\nSkills\nPython, LLM, LangChain, RAG, Generative AI, Data Science, VectorDB, LLama","description_format":"text","description_chars":7876,"description_truncated":false,"requirements":{"experience_years_min":8,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["AI Consulting & Integration"],"lifecycle":[{"event":"open","at":"2026-09-25T13:06:44Z"}],"liveness":{"score":86,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.859,"p_room":1,"age_days":6,"expected_fill_days":24,"reasons":["seen:6","velocity","win:early"],"computed_at":"2026-10-01T05:45:00Z"},"pay":{"stated_usd_annual":52410,"is_top_pay":false},"html_url":"https://alion.io/job/latentbridge-technical-lead","json_url":"https://alion.io/job/latentbridge-technical-lead.json","meta":{"generated_at":"2026-10-01T11:24:17Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":3047,"day_limit":5000,"remaining_today":1953,"minute_limit":60,"resets_at":"2026-10-02T00:00:00Z"}}}