Tech Lead - AI / ML

Connecttel; Chennai District; On-site; INR 2,500,000 - 6,000,000; Full time

Job summary

Connecttel in Chennai seeks an AI/ML Tech Lead to own the technical execution of core AI platforms, bridging architectural vision with code-level delivery. Lead an agile pod building enterprise-grade Agentic Workflows, Knowledge Graphs, and AI security safeguards.

You will drive hands-on engineering, translate blueprints into scalable, production-ready features, and mentor the team to uphold engineering excellence with CI/CD and robust error handling.

Qualifications

• 5+ years of core software engineering experience, incl. 3+ years in AI/ML or LLM-based production.
• Leadership: proven track record guiding agile pods and mentoring developers.
• Master’s in CS/Data Science/AI or equivalent practical experience with shipped products.

Responsibilities

• Architectural leadership and pod execution to ship low-latency AI features.
• Hands-on development of agentic workflows, knowledge systems, and multi-modal LLMs.
• Design and enforce AI security guardrails and robust CI/CD testing.
• MLOps, eval, cost optimization, latency tracking across multi-cloud.
• Cross-functional collaboration with product, solution architects, and clients.

Skills

Leadership

Architectural design

Mentoring

Agile teams

Technical communication

Education

Master's in CS/Data Science/AI

Tools

Python

TypeScript

Go

Java

LangGraph

AutoGen

LangChain

LlamaIndex

PyTorch

Hugging Face

Qdrant

Pinecone

Milvus

Neo4j

RDF/Ontologies

Docker

Kubernetes

GitHub Actions

MLflow

Weights & Biases

Job description

AI/ML Tech Lead

Experience Level: 6+ Years

Location: Chennai

Full Time

Role Overview

We are seeking a high-ownership, hands‑on AI / LLM Engineering Tech Lead to drive the technical execution of our core AI platforms. In this role, you will bridge deep architectural vision with direct code-level execution. You will lead an agile engineering pod building enterprise‑grade Agentic Workflows, Knowledge Graphs, and AI Security Safeguards.

If you are driven by passion and innovation, determined to bend the limits to build something truly transformative this role is for you.

Key Responsibilities

• Architectural Leadership & Pod Execution

Lead an engineering pod (AI/ML Engineers, Full‑Stack Developers, and DevOps) to ship low‑latency, production‑ready AI features.

Translate high‑level blueprints into actionable technical specifications, clean codebases, and sprint backlogs.

Enforce engineering excellence through code reviews, automated CI/CD testing protocols, and robust error‑handling standards.

• Hands‑On Agentic & Knowledge Systems Development

Architect & Code: Build multi-modal LLM workflows and autonomous agentic systems using modern orchestration frameworks.

Knowledge Layer Integration: Implement knowledge graphs, dynamic ontologies, and advanced vector retrieval strategies (Hybrid Search, Graph RAG, Re‑ranking) that go beyond standard naive RAG.

AI Security & Guardrails

Deploy active safeguards against prompt injection, model jailbreaks, hallucination, and data leakage using core AI Security principles.

• Production MLOps, Eval & Performance

LLM Ops: Build automated pipelines for continuous model evaluation (e.g. RAGAS, TruLens), dynamic prompt versioning, and latency tracking.

Cost & Throughput Optimization: Optimize token consumption, context window management, caching, and model inference costs across multi‑cloud deployments.

Observability: Monitor model drift, data distribution shifts, and edge‑case execution in live enterprise production environments.

• Cross‑Functional Execution

Collaborate closely with Product Managers, Solution Architects, and client teams to resolve complex edge cases and accelerate feature delivery.

Serve as a technical mentor, elevating team execution standards and unblocking complex algorithmic or system challenges daily.

Required Qualifications

Education & Experience:

Experience: 5+ years of core software engineering experience, including 3+ years specifically architecting and delivering AI/ML or LLM‑based products into production.

Leadership: Proven track record leading agile pods, conducting technical design reviews, and mentoring developers.

Education: Master’s in Computer Science, Data Science, AI, or equivalent practical experience demonstrated through shipped products or open‑source contributions and professional certifications.

Technical Stack Requirements:

Languages & Core CS: Strong mastery of Python (FastAPI, PyDantic, Asyncio) with familiarity in TypeScript, Go, or Java.

Agentic Frameworks & AI Stack: Hands‑on experience with modern LLM orchestration tools (LangGraph, AutoGen, CrewAI, LangChain, LlamaIndex), PyTorch, Hugging Face, and major LLM Provider APIs.

Vector Engines & Knowledge Graphs: Direct working experience with vector databases (Qdrant, Pinecone, Milvus, Weaviate) and Knowledge Graph technologies (Neo4j, RDF/Ontologies).

AI Security & Guardrails: Familiarity with adversarial prompt testing, red teaming concepts, and guardrail implementation.

MLOps & Infra: Practical experience with Docker, Kubernetes, GitHub Actions, MLflow, Weights & Biases, and serverless AI infrastructure on AWS/GCP/Azure.

Soft Skills:

Strong technical articulation and communication skills to engage with technical stakeholders, understand requirements, and present engineering solutions cleanly.