Job Description :

We are looking for a Senior Lead Engineer AI/ML & GenAI to lead the design, development, and deployment of production-grade AI, Machine Learning, and Generative AI solutions. The role requires strong hands-on expertise in Python, SQL, Machine Learning, Deep Learning, NLP, LLMs, RAG, Prompt Engineering, LangChain, LangGraph, and AWS cloud services.

The candidate will work on scalable AI/ML applications, intelligent automation, LLM-powered solutions, and cloud-native microservices. The role involves taking AI solutions from experimentation and prototyping through production deployment, monitoring, optimization, and continuous improvement.

Key Responsibilities :

AI/ML & Deep Learning :

- Design, develop, train, evaluate, and deploy Machine Learning and Deep Learning models.

- Work on supervised and unsupervised learning problems across structured and unstructured datasets.

- Apply NLP techniques for text classification, information extraction, semantic analysis, and other language-based use cases.

- Develop predictive models using Scikit-Learn and gradient-boosting frameworks such as XGBoost, LightGBM, and CatBoost.

- Perform feature engineering, model evaluation, hyperparameter tuning, and model optimization.

- Analyze model performance and implement improvements to accuracy, scalability, and reliability.

- Translate business requirements into scalable AI/ML solutions.

Generative AI & LLM :

- Design and develop Generative AI applications using Large Language Models.

- Build LLM-powered applications using RAG, prompt engineering, and contextual retrieval techniques.

- Develop and optimize prompts for different business and technical use cases.

- Implement document question-answering, knowledge assistants, summarization, classification, extraction, and conversational AI solutions.

- Work with LangChain and LangGraph to develop agentic and LLM-based workflows.

- Evaluate LLM responses for accuracy, relevance, consistency, and reliability.

- Implement techniques to improve LLM grounding, context management, and response quality.

RAG & AI Application Development :

- Design Retrieval-Augmented Generation (RAG) pipelines for enterprise applications.

- Work with document ingestion, chunking, embedding generation, retrieval, reranking, and response generation.

- Integrate vector databases and search technologies into GenAI applications.

- Develop multi-step AI workflows and agent-based applications.

- Explore advanced approaches such as GraphRAG where applicable.

- Ensure AI solutions are scalable, secure, maintainable, and production-ready.

AWS Cloud & Deployment :

- Design and deploy AI/ML applications on AWS.

- Work with AWS EKS for containerized application deployment and orchestration.

- Develop serverless components using AWS Lambda.

- Use Amazon S3 for data and model artifact storage.

- Implement event-driven architectures using SNS and SQS.

- Develop and integrate APIs using Amazon API Gateway.

- Support deployment, monitoring, scaling, and troubleshooting of AI/ML workloads on AWS.

- Follow cloud architecture and security best practices.

Backend & Microservices :

- Develop scalable backend services using Python.

- Design and build REST APIs for AI/ML and GenAI applications.

- Develop microservices-based architectures for AI solutions.

- Containerize applications using Docker.

- Integrate AI/ML services with enterprise applications and data platforms.

- Implement robust error handling, logging, monitoring, and service-level controls.

Data & SQL :

- Write efficient SQL queries for data extraction, transformation, validation, and analysis.

- Work with structured and unstructured data sources.

- Perform data preprocessing, cleaning, transformation, and feature engineering.

- Ensure data quality and consistency for ML and GenAI pipelines.

- Work with large datasets and optimize data processing workflows.

Development & DevOps :

- Use Git for source-code management and collaborative development.

- Follow software engineering best practices for version control, code reviews, testing, and deployment.

- Work with CI/CD pipelines for automated application deployment.

- Collaborate with DevOps, Data Engineering, Product, and Business teams.

- Maintain technical documentation covering architecture, code, APIs, models, and deployment processes.

Technical Leadership :

- Lead technical design and implementation of AI/ML and GenAI solutions.

- Mentor engineers and provide technical guidance on AI/ML development practices.

- Review code, architecture, model implementations, and technical designs.

- Drive engineering best practices across AI/ML projects.

- Collaborate with stakeholders to understand requirements and translate them into technical solutions.

- Participate in solution design, technical discussions, estimation, and delivery planning.

- Troubleshoot complex technical issues across application, cloud, data, and AI layers.

Educational Qualifications :

- Bachelor's or Master's degree in Computer Science, Information Technology, Artificial Intelligence, Data Science, Engineering, or a related field.

- 8 - 10 years of relevant experience in AI/ML engineering, with strong hands-on experience in Generative AI and production-grade application development.

Key Competencies :

- Strong problem-solving and analytical skills.

- Strong software engineering fundamentals.