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
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Lead the technical design and implementation of enterprise-scale data and AI solutions, ensuring alignment with business objectives and technology standards
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Review solution designs, code, and technical deliverables to ensure quality, performance, and adherence to architectural standards
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Mentor and guide development teams by resolving technical challenges and ensuring best practices
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Design and implement production-ready applications using LLMs (GPT-4, Claude, Gemini) and other foundation models.
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Build and optimize RAG (Retrieval-Augmented Generation) systems using vector databases like Pinecone, Weaviate, or Qdrant.
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Design and implement hybrid RAG systems, specifically utilizing LightRAG/GraphRAG (Knowledge Graphs) alongside vector databases to enable multi-hop reasoning across complex operational data.
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Implement model fine-tuning pipelines for domain-specific applications using techniques like LoRA and QLoRA.
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Create multi-agent systems and complex AI workflows using frameworks like LangGraph or AWS Strands, including Amazon Bedrock for foundation models.
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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.
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Architect secure, event-driven integrations between the AI platform and Enterprise ITSM tools (e.g., Jira, MS Teams, GitLab) using webhooks and message brokers.
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Implement guardrails and safety measures to ensure responsible AI deployment.
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Model Development: Design and implement AI/ML models, algorithms, and pipelines.
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Collaboration: Work with data scientists, engineers, and business stakeholders to deliver AI-driven solutions.
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Optimization & Evaluation: Continuously monitor and improve AI systems for accuracy and efficiency.
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Compliance & Ethics: Ensure AI solutions adhere to ethical standards and regulatory requirements (GDPR, fairness, bias mitigation).
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Leadership: Guide cross-functional teams and mentor junior engineers in AI best practices
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Security & Compliance: Data anonymization, secure model deployment, bias detection
Mandatory Skills
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Programming Languages: 5+ years of Python development experience with strong software engineering fundamentals.
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Bachelor's or Master's degree in Computer Science, AI/ML, or equivalent practical experience
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Hands-on experience building applications with LLM APIs (OpenAI, Anthropic, Google, etc.)
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Machine Learning & Deep Learning: Expertise in TensorFlow, PyTorch, Hugging Face; model selection, evaluation, and interpretability.
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Strong knowledge of prompt engineering techniques and in-context learning.
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Experience with vector databases and embedding models for semantic search.
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Experience with Graph databases (e.g., Neo4j) and implementing GraphRAG /LightRAG architectures (e.g., LightRAG).
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Experience with AI memory management frameworks (e.g., Mem0) to maintain stateful, multi-turn conversational context and episodic memory.
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Familiarity with cloud platforms (AWS, GCP, Azure) and containerization (Docker, Kubernetes).
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Deep hands-on experience with AWS, specifically Amazon EKS, Istio / Kubernetes Gateway API, and managing real-time WebSocket connections.
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Excellent problem-solving skills and ability to work with ambiguous requirements.
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Strong communication skills to explain complex AI concepts to various stakeholders.
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Security & Compliance: Data anonymization, secure model deployment, bias detection
