Job Description

Job Summary

We are looking for a highly skilled Python AI/ML Engineer to design, develop, and deploy machine learning and Generative AI solutions. The ideal candidate should possess strong expertise in Python development, Machine Learning, Deep Learning, Large Language Models (LLMs), and cloud-based AI platforms. The role involves building scalable AI solutions, integrating ML models into enterprise applications, and driving innovation through advanced AI technologies.

Must-Have Skills

• Python Programming (Advanced)
• Machine Learning (Scikit-Learn, XGBoost, LightGBM)
• Deep Learning (TensorFlow, PyTorch)
• Generative AI and Prompt Engineering
• Large Language Models (OpenAI, Azure OpenAI, Gemini, Claude, Llama)
• LangChain / LangGraph
• RAG (Retrieval Augmented Generation)
• Vector Databases (Pinecone, ChromaDB, Weaviate, FAISS)
• REST API Development (FastAPI, Flask)
• SQL and NoSQL Databases
• Git, CI/CD and DevOps Practices
• AWS / Azure / GCP AI Services

Good-to-Have Skills

• Agentic AI Frameworks
• MLOps / LLMOps
• Kubernetes & Docker
• Apache Spark / Databricks
• Knowledge Graphs
• NLP and Computer Vision
• GitHub Copilot
• Data Engineering Pipelines

Key Responsibilities

• Design, develop, and deploy AI/ML and Generative AI solutions for enterprise applications.
• Build and optimise machine learning models for prediction, recommendation, classification, and automation use cases.
• Develop GenAI applications using LLMs, RAG frameworks, vector databases, and AI agents.
• Create scalable APIs and microservices using FastAPI or Flask.
• Implement MLOps practices for model deployment, monitoring, and lifecycle management.
• Fine-tune and evaluate LLMs and foundation models.
• Collaborate with business stakeholders to convert AI use cases into production-ready solutions.
• Ensure AI solutions meet security, governance, and performance requirements.
• Participate in architecture reviews, code reviews, and best-practice initiatives.

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Data Science, AI/ML, or related field.
• 5+ years of hands-on Python development experience.
• 2+ years of experience in AI/ML or Generative AI projects.
• Strong understanding of ML algorithms, model evaluation, and data engineering concepts.
• Experience working with cloud AI services on AWS, Azure, or GCP.
• Strong problem-solving and communication skills.

Preferred Experience

• Developing enterprise AI assistants, copilots, chatbots, and GenAI applications.
• Implementing RAG-based solutions using enterprise knowledge repositories.
• Deploying ML models using MLOps tools and cloud platforms.
• Working in Agile/Scrum environments.

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Areas of responsibility

Key Responsibilities

• Design, develop, and deploy AI/ML and Generative AI solutions for enterprise applications.
• Build and optimise machine learning models for prediction, recommendation, classification, and automation use cases.
• Develop GenAI applications using LLMs, RAG frameworks, vector databases, and AI agents.
• Create scalable APIs and microservices using FastAPI or Flask.
• Implement MLOps practices for model deployment, monitoring, and lifecycle management.
• Fine-tune and evaluate LLMs and foundation models.
• Collaborate with business stakeholders to convert AI use cases into production-ready solutions.
• Ensure AI solutions meet security, governance, and performance requirements.
• Participate in architecture reviews, code reviews, and best-practice initiatives.

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Gathering of requirements-Assist in conducting workshops and discussions with client stakeholders to gather business process requirements.

Coding and Configuration-"Develop and deliver code for assigned modules, in alignment with client requirements ensuring proper integration with system components.

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Testing and Trial Runs-"Execute testing for assigned modules, validate functionality across required interactions, and participate in User Acceptance Testing (UAT) sessions.

Implementation-Participate in implementation of software application and its integration with other systems to ensure stability.

Maintain Documentation-Prepare detailed reports and documentation on project specifications, activities, and status, while presenting information using flowcharts, layouts, diagrams, code comments, and clear, well-structured code.

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Stakeholder Collaboration-Collaborate with cross functional teams  to understand requirements, support development activities, check system compatibility, and ensure solutions meet expected standards.