Data Scientist

We are looking for a highly motivated and hands-on AI Engineer with strong application development experience and expertise in Generative AI and Agentic AI. The ideal candidate should be capable of designing, developing, and deploying enterprise-grade AI solutions that leverage Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, and modern cloud-native architectures.

The candidate should have a solid understanding of how AI agents work, common design patterns, orchestration approaches, security and governance considerations, and the challenges associated with deploying AI solutions in production environments.

Key Responsibilities

• Design, develop, and deploy enterprise AI applications and services.
• Build and implement Generative AI solutions using LLMs, RAG, embeddings, and vector databases.
• Develop and orchestrate AI agents and multi-agent workflows for business process automation.
• Integrate AI solutions with enterprise applications, APIs, data platforms, and business systems.
• Design AI solutions with appropriate guardrails, governance, security, and human-in-the-loop controls.
• Evaluate and improve model performance, prompt effectiveness, and user experience.
• Implement monitoring, observability, testing, and evaluation frameworks for AI applications.
• Collaborate with product owners, architects, and engineering teams to translate business requirements into scalable AI solutions.
• Contribute to reusable AI frameworks, accelerators, and platform capabilities.
• Stay current with emerging trends in Generative AI, Agentic AI, and enterprise AI technologies.

Required Skills & Qualifications

Experience

• 5-8 years of overall software engineering experience.
• Minimum 2+ years of hands-on experience with Generative AI solutions.
• Experience building and supporting production-grade applications.

Application Development

• Strong proficiency in Python.
• Hands-on experience with FastAPI, Flask, or similar backend frameworks.
• Experience developing REST APIs and microservices.
• Familiarity with cloud-native development and deployment practices.
• Good understanding of Git, CI/CD pipelines, testing, and software engineering best practices.

Generative AI & Agentic AI

Strong understanding of:

• Large Language Models (LLMs)
• Prompt Engineering
• Retrieval-Augmented Generation (RAG)
• Embeddings and Vector Databases
• AI Evaluation Techniques
• Agentic AI Concepts and Design Patterns
• Multi-Agent Systems and Agent Orchestration
• Tool Calling and Function Calling

Hands-on experience with one or more of the following is preferred:

• LangChain
• LangGraph
• Semantic Kernel
• Microsoft AI Foundry / Azure AI Foundry
• AutoGen or equivalent agent frameworks

Cloud & AI Platforms

Experience working with one or more cloud platforms:

• Microsoft Azure
• AWS
• Google Cloud Platform (GCP)

Experience with:

• Azure OpenAI or equivalent LLM services
• Azure AI Search or similar search platforms
• Vector Databases and semantic retrieval solutions

Agent Governance & Reliability

Good understanding of:

• Agent memory and context management
• Hallucination mitigation approaches
• Security and access controls
• Human-in-the-loop workflows
• Prompt injection and AI security risks
• Agent monitoring and observability
• Responsible AI principles
• Cost optimisation and governance

Good to Have

• Knowledge of Knowledge Graphs and Business Ontologies.
• Understanding of enterprise data platforms and data ecosystems.
• Experience with or understanding of graph databases.
• Experience building AI copilots, enterprise assistants, or conversational AI solutions.
• Exposure to React or other modern front-end development frameworks.

Desired Attributes

• Strong analytical and problem-solving skills.
• Excellent communication and collaboration abilities.
• Ability to independently drive solutions from concept to production.
• Passion for emerging AI technologies and continuous learning.
• Pragmatic mindset with a strong focus on business outcomes.
• Ability to balance innovation with engineering quality and operational excellence.

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