Job Title: Lead AI Engineer ? Role Overview We are seeking a highly experienced and hands-on Lead AI Engineer to architect, build, and scale production-grade Artificial Intelligence and Machine Learning solutions. This role requires deep technical expertise, strong engineering discipline, and the ability to guide teams in translating AI concepts into secure, reliable, and business-ready platforms.

You will lead advanced AI workloads including Generative AI application development, Retrieval-Augmented Generation, agentic workflows, predictive modeling, and intelligent automation. You will also provide technical direction, mentor engineers, establish engineering best practices, and ensure AI solutions deliver measurable impact across digital platforms.

?️ Key Responsibilities

- Technical Leadership: Lead the design, development, and deployment of scalable AI and Generative AI solutions, providing technical direction across architecture, implementation, and production readiness.
- AI Solution Architecture: Architect robust Retrieval-Augmented Generation pipelines, vector search systems, model orchestration layers, and enterprise-grade AI services.
- Agentic & Autonomous Workflows: Design and guide the development of multi-agent AI systems capable of executing complex business processes, tool use, reasoning, and workflow automation.
- Platform & API Engineering: Build and oversee secure, scalable REST or gRPC services that expose AI capabilities to digital products, backend platforms, and enterprise systems.
- Evaluation, Governance & Guardrails: Establish AI evaluation frameworks, quality benchmarks, content safety controls, hallucination mitigation strategies,



and responsible AI practices.
- Performance & Cost Optimization: Optimize models, prompts, embeddings, inference workloads, and infrastructure to improve latency, reliability, token efficiency, and cloud cost effectiveness.
- Mentorship & Engineering Excellence: Mentor engineers, conduct technical reviews, define coding standards, and promote reusable AI components, testing practices, documentation, and operational discipline.

? Required Technical Skills

- Programming Languages: Expert proficiency in Python with strong software engineering fundamentals, clean architecture, testing practices, and performance-aware development.
- AI Frameworks: Advanced hands-on experience with LangChain, LlamaIndex, AutoGen, or similar orchestration frameworks, along with deep learning frameworks such as PyTorch or TensorFlow.
- LLMs & Foundation Models: Proven experience selecting, fine-tuning, prompting, evaluating, and deploying commercial and open-weight models including OpenAI, Anthropic, Llama, Mistral, vLLM, Ollama, or equivalent platforms.
- Vector Databases: Strong experience designing semantic search and retrieval systems using Pinecone, Milvus, Qdrant, pgvector, or similar vector database technologies.
- MLOps & Cloud Infrastructure: Strong experience with model lifecycle management,



deployment automation, observability, CI/CD, containerization, and cloud AI platforms such as AWS SageMaker, AWS Bedrock, or equivalent services.
- Data Engineering & Pipelines: Solid understanding of SQL, distributed data processing, orchestration tools such as Apache Airflow or Prefect, and reliable data ingestion for AI training, retrieval, and contextual grounding.

? Qualifications & Experience

- Experience: 8+ years of professional experience in software engineering, machine learning, or applied AI, with at least 2–3+ years building and shipping Generative AI, LLM, or ML products into production.
- Industry Background: Experience delivering AI systems in regulated or large-scale digital environments such as Fintech, Banking, E-commerce, Healthcare, or enterprise platforms is highly preferred.
- Education: Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or an equivalent technical discipline; advanced certifications or open-source contributions are a plus.

? Core Competencies

- Technical Ownership: Ability to own complex AI platforms end to end, from problem framing and architecture through delivery, monitoring, optimization, and continuous improvement.
- Responsible AI Leadership: Robust awareness of data privacy, bias mitigation, security, explainability, model governance, and safe deployment practices.
- Mentorship & Collaboration: Ability to mentor engineers, influence cross-functional stakeholders, communicate complex AI concepts clearly, and drive practical adoption of emerging AI technologies.

📌 Lead - AI Engineer / DVP - AI Engineering (Bengaluru)
🏢 Emkay Global Financial Services
📍 Bengaluru