:

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 cooperative 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.

📌 Altezzasys - Senior Lead Engineer - AI/ML (India)
🏢 Altezzasys
📍 India