Senior Lead Engineer - AI

Quest Global; Bengaluru; On-site; INR 2,500,000 - 5,000,000; Full time

Job summary

Quest Global in Bengaluru is looking for a Senior Lead AI Engineer to drive the end-to-end delivery of complex AI projects. You will lead technical design, collaborate with cross-functional teams, and ensure scalable, high-quality AI solutions are delivered on time.

The role requires deep expertise in LLMs, RAG architectures, production-grade pipelines, and strong leadership to mentor engineers and align with business objectives.

Qualifications

• 5+ years of Python development with solid software fundamentals.
• Degree in Computer Science, AI/ML or equivalent.
• Hands-on with LLM APIs (OpenAI, Anthropic, Google).
• Expert in TensorFlow, PyTorch, HuggingFace.
• Experience with vector databases and embeddings.
• Experience with Graph databases (Neo4j) and GraphRAG/LightRAG.
• Cloud platforms (AWS, GCP, Azure) and containers (Docker, Kubernetes).
• AWS EKS, Istio/Gateway API, and real-time WebSocket knowledge.
• Excellent problem solving and communication.

Responsibilities

• Lead the technical design and implementation of enterprise-scale AI solutions.
• Review designs, code, and deliverables for quality and standards.
• Mentor development teams on best practices.
• Design and implement production-ready applications using LLMs.
• Build and optimize RAG systems with vector databases.
• Design hybrid RAG with LightRAG and knowledge graphs.
• Implement fine-tuning pipelines (LoRA/QLoRA).
• Create multi-agent AI workflows with LangGraph or AWS Strands.
• Integrate multimodal models and protocols like MCP/A2A.
• Architect secure, event-driven integrations with Jira/MS Teams/GitLab.
• Implement guardrails and safety for responsible AI.
• Collaborate with data scientists and stakeholders.
• Monitor and improve AI systems for accuracy and efficiency.
• Ensure GDPR/compliance and ethical AI practices.
• Lead cross-functional teams and mentor engineers.
• Ensure secure deployment and data anonymization.

Skills

Python

LLM APIs

TensorFlow

PyTorch

HuggingFace

Prompt engineering

Vector DBs

Graph DBs

AWS

Kubernetes

Distributed systems

Security

Education

Bachelors/Masters in CS or AI/ML

Tools

AWS

Docker

Kubernetes

Job description

Job Summary:

We are seeking a Senior Lead AI Engineer, to drive the end-to-end delivery of complex AI project. The ideal candidate will lead technical design and implementation, collaborate with cross-functional stakeholders, conduct architecture and code reviews, provide technical leadership to engineering teams, and ensure scalable, high-quality AI solutions are delivered successfully.

Job Requirements

Job Summary:

We are seeking a Senior Lead AI Engineer, to drive the end-to-end delivery of complex AI project. The ideal candidate will lead technical design and implementation, collaborate with cross-functional stakeholders, conduct architecture and code reviews, provide technical leadership to engineering teams, and ensure scalable, high-quality AI solutions are delivered successfully.

EXP – 9 to 12 years

Key Responsibilities

• Lead the technical design and implementation of enterprise-scale data and AI solutions, ensuring alignment with business objectives and technology standards
• Review solution designs, code, and technical deliverables to ensure quality, performance, and adherence to architectural standards
• Mentor and guide development teams by resolving technical challenges and ensuring best practices
• Design and implement production-ready applications using LLMs (GPT-4, Claude, Gemini) and other foundation models.
• Build and optimize RAG (Retrieval-Augmented Generation) systems using vector databases like Pinecone, Weaviate, or Qdrant.
• Design and implement hybrid RAG systems, specifically utilizing LightRAG/GraphRAG (Knowledge Graphs) alongside vector databases to enable multi-hop reasoning across complex operational data.
• Implement model fine-tuning pipelines for domain-specific applications using techniques like LoRA and QLoRA.
• Create multi-agent systems and complex AI workflows using frameworks like LangGraph or AWS Strands, including Amazon Bedrock for foundation models.
• Integrate multiple AI models (text, vision, audio) to create multimodal applications and work with protocols like MCP and A2A to extend the capabilities of the system.
• Architect secure, event-driven integrations between the AI platform and Enterprise ITSM tools (e.g., Jira, MS Teams, GitLab) using webhooks and message brokers.
• Implement guardrails and safety measures to ensure responsible AI deployment.
• Model Development: Design and implement AI/ML models, algorithms, and pipelines.
• Collaboration: Work with data scientists, engineers, and business stakeholders to deliver AI-driven solutions.
• Optimization & Evaluation: Continuously monitor and improve AI systems for accuracy and efficiency.
• Compliance & Ethics: Ensure AI solutions adhere to ethical standards and regulatory requirements (GDPR, fairness, bias mitigation).
• Leadership: Guide cross-functional teams and mentor junior engineers in AI best practices
• Security & Compliance: Data anonymization, secure model deployment, bias detection

Mandatory Skills

• Programming Languages: 5+ years of Python development experience with strong software engineering fundamentals.
• Bachelor's or Master's degree in Computer Science, AI/ML, or equivalent practical experience
• Hands-on experience building applications with LLM APIs (OpenAI, Anthropic, Google, etc.)
• Machine Learning & Deep Learning: Expertise in TensorFlow, PyTorch, Hugging Face; model selection, evaluation, and interpretability.
• Strong knowledge of prompt engineering techniques and in-context learning.
• Experience with vector databases and embedding models for semantic search.
• Experience with Graph databases (e.g., Neo4j) and implementing GraphRAG /LightRAG architectures (e.g., LightRAG).
• Experience with AI memory management frameworks (e.g., Mem0) to maintain stateful, multi-turn conversational context and episodic memory.
• Familiarity with cloud platforms (AWS, GCP, Azure) and containerization (Docker, Kubernetes).
• Deep hands-on experience with AWS, specifically Amazon EKS, Istio / Kubernetes Gateway API, and managing real-time WebSocket connections.
• Excellent problem-solving skills and ability to work with ambiguous requirements.
• Strong communication skills to explain complex AI concepts to various stakeholders.
• Security & Compliance: Data anonymization, secure model deployment, bias detection