Lead - AI Engineer / DVP - AI Engineering

Job Title: Lead AI Engineer

Role Overview

We are seeking a highly experienced and hands-on Lead AIEngineer to architect, build, and scale production-grade ArtificialIntelligence and Machine Learning solutions. This role requires deep technicalexpertise, strong engineering discipline, and the ability to guide teams intranslating AI concepts into secure, reliable, and business-ready platforms.

You will lead advanced AI workloads including Generative AIapplication development, Retrieval-Augmented Generation, agentic workflows,predictive modeling, and intelligent automation. You will also providetechnical 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 AIsolutions, providing technical direction across architecture, implementation,and production readiness.

AI SolutionArchitecture: Architect robust Retrieval-Augmented Generation pipelines,vector search systems, model orchestration layers, and enterprise-grade AIservices.

Agentic AutonomousWorkflows: Design and guide the development of multi-agent AI systemscapable of executing complex business processes, tool use, reasoning, andworkflow automation.

Platform APIEngineering: Build and oversee secure, scalable REST or gRPC services thatexpose AI capabilities to digital products, backend platforms, and enterprisesystems.

Evaluation, GovernanceGuardrails: Establish AI evaluation frameworks, quality benchmarks,content safety controls, hallucination mitigation strategies, and responsibleAI practices.

Performance CostOptimization:



Optimize models, prompts, embeddings, inference workloads,and infrastructure to improve latency, reliability, token efficiency, and cloudcost effectiveness.

Mentorship Engineering Excellence: Mentor engineers, conduct technical reviews, definecoding standards, and promote reusable AI components, testing practices,documentation, and operational discipline.

Required TechnicalSkills

Programming Languages: Expert proficiency in Python with strong software engineeringfundamentals, clean architecture, testing practices, and performance-awaredevelopment.

AI Frameworks: Advanced hands-on experience with LangChain, LlamaIndex , AutoGen, orsimilar orchestration frameworks, along with deep learning frameworks such as PyTorch or TensorFlow.

LLMs FoundationModels: 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 CloudInfrastructure: Strong experience with model lifecycle management,deployment automation, observability, CI/CD, containerization, and cloud AIplatforms 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 dataingestion for AI training, retrieval, and contextual grounding.

Qualifications Experience

Experience: 8+ years of professional experience insoftware engineering, machine learning, or applied AI, with at least 23+ yearsbuilding and shipping Generative AI, LLM, or ML products into production.

Industry Background: Experience delivering AI systems in regulated or large-scale digitalenvironments such as Fintech, Banking, E-commerce, Healthcare, or enterpriseplatforms is highly preferred.

Education: Bachelors or Masters degree in Computer Science, Artificial Intelligence,Data Science, Engineering, or an equivalent technical discipline; advancedcertifications or open-source contributions are a plus.

Core Competencies

Technical Ownership: Ability to own complex AI platforms end to end, from problem framing andarchitecture through delivery, monitoring, optimization, and continuousimprovement.

Responsible AILeadership: Robust awareness of data privacy, bias mitigation, security,explainability, model governance, and safe deployment practices.

Mentorship Collaboration: Ability to mentor engineers, influence cross-functionalstakeholders, communicate complex AI concepts clearly, and drive practicaladoption of emerging AI technologies.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

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