Technical Lead

Job Summary – AI Technical Lead.

• Kumaran Systems is looking for an experienced AI Lead (Technical) to lead the design, development, and deployment of scalable, production-ready AI/ML solutions within its Equity Market Technology -Capital Markets domain.
• The role will drive innovation in Generative AI, Retrieval-Augmented Generation (RAG), Agentic AI workflows, and intelligent automation using the Microsoft Azure cloud platform.
• The candidate will provide technical leadership to a team of AI engineers, collaborate closely with stakeholders, data scientists, and platform teams, and translate business requirements into robust, secure, and scalable AI solutions.
• The role requires strong hands-on technical expertise, solution architecture capabilities, and the ability to lead AI initiatives from Concept to Production.

Details of Employment:

Location: Chennai, India

Work Location – Guindy Olympia Tech Park OR SIPCOT Siruseri.

Mode of Employment - 3 days Hybrid in the Office.

Employment Type: Full-Time, Permanent.

Notice Period - 0 to 15 days from the date of the offer (Critical Requisition; hence the ask)

Key Responsibilities

1. AI Architecture and Solution Design

• Architect and lead the development of enterprise-grade RAG pipelines, including document ingestion, vector databases, embedding models, and retrieval strategies using Azure AI Search, FAISS, and Pinecone.
• Design and implement Agentic AI systems incorporating autonomous agents, multi-step planning, tool orchestration, reasoning workflows, and human-in-the-loop controls.
• Develop AI solutions that integrate with internal systems, including trading platforms, operations workbenches, and risk engines, to automate decision support, exception handling, and reporting.
• Establish standards for prompt engineering, AI guardrails, and evaluation frameworks, including offline evaluations, A/B testing, and automated regression testing.

2. Azure Cloud and AI Platform Engineering

• Lead the design, implementation, and management of AI/ML infrastructure on Microsoft Azure using Azure OpenAI Service, Azure AI Search, Azure Machine Learning, Azure Functions, and Azure Kubernetes Service (AKS).
• Design scalable, reliable, and cost-efficient AI inference pipelines using caching, request batching, and token-budget optimization.
• Implement CI/CD pipelines for ML models and AI agents, incorporating model versioning, controlled deployments, A/B testing, rollback mechanisms, and observability.
• Ensure compliance with enterprise cloud governance, data residency, and security requirements using Azure Private Endpoints, Managed Identity, Key Vault, and related security controls.

3. AI Performance Optimization and Troubleshooting

• Diagnose and resolve production AI performance issues, including inference latency, embedding drift, retrieval quality degradation, hallucinations, and context-window limitations.
• Monitor and optimize token consumption, API rate limits, throughput, and cost per query across production AI workloads.
• Establish AI observability through structured logging, distributed tracing across prompt processing, retrieval, and generation, and dashboards tracking latency (p50/p95/p99), retrieval recall, and answer relevance.
• Conduct root-cause analysis of AI system failures, including incorrect tool invocations, RAG retrieval failures, safety-filter false positives, and model regressions following version upgrades.

4. Technical Leadership and Collaboration

• Lead and mentor a team of 4–6 AI/ML engineers, overseeing code reviews, architecture decisions, sprint planning, engineering standards, and professional development.
• Collaborate with business stakeholders, data scientists, quantitative teams, and operations leaders to translate business requirements into scalable, production-ready AI solutions.
• Evaluate emerging AI technologies, model releases, fine-tuning techniques, agent frameworks such as LangChain, Semantic Kernel, AutoGen, and CrewAI, and AI evaluation methodologies.
• Represent the AI engineering function in technical design reviews, architecture governance forums, and incident post-mortems.
• Drive engineering best practices and continuous improvement across AI solution design, development, deployment, monitoring, security, and operational support.

Required Skills and Qualifications

• 10+ years of software engineering experience, with 4+ years focused on AI/ML solutions in production environments.
• Deep hands-on experience with RAG architectures, including vector databases, embedding pipelines, chunking strategies, hybrid search (keyword + semantic), re-ranking, and retrieval evaluation.
• Proven track record of building Agentic AI systems, including multi-agent orchestration, tool-use patterns, planning loops, memory management, and fallback strategies.
• Strong proficiency in Azure Cloud services, including Azure OpenAI Service, Azure AI Search (formerly Cognitive Search), Azure Machine Learning, Azure Functions, AKS, and App Service.
• Experience troubleshooting AI performance issues, including inference latency optimization, token budgeting, embedding drift detection, hallucination mitigation, and cost management at scale.
• Proficiency in Python and at least one of the following: C#/.NET or TypeScript/Node.js.
• Experience with LLM frameworks such as LangChain, Semantic Kernel, LlamaIndex, AutoGen, or CrewAI.
• Solid understanding of ML fundamentals, including transformers, attention mechanisms, fine-tuning (LoRA/QLoRA), RLHF, and evaluation metrics such as BLEU, ROUGE, BERTScore, faithfulness, and relevance.
• Experience leading engineering teams of 4+ members in an Agile/Scrum environment.
• Strong communication skills, with the ability to explain AI concepts to non-technical stakeholders and prepare clear technical documentation.

Resumes can be shared with devika.raju@kumaran.com