Job Description

We are seeking a highly skilled AI Engineer (Agentic AI) to support the design and delivery of next-generation AI solutions leveraging large language models (LLMs), autonomous agents, and advanced machine learning techniques. The role will enable AI-driven transformation by building intelligent systems such as copilots, multi-agent workflows, and RAG solutions.

About the Role

This role sits within the Technology Consulting – Data & AI practice. The candidate will translate business challenges into AI use cases and deliver production-grade AI systems across the full lifecycle.

Key Responsibilities

- Design and develop AI agents and multi-agent systems

- Build RAG pipelines

- Develop scalable AI services using APIs and microservices

- Integrate LLMs with enterprise systems

- Apply Responsible AI practices

- Engage with stakeholders and present solutions

Required Experience & Qualifications

- 2–6 years of experience in AI engineering or ML

Key Skills & Competencies

1 - Agentic AI & LLM Eng – Mandatory – Weight 10 – Core Competencies:

• AI Agents
• Autonomous Agents
• Multi-agent systems
• LLM Applications
• LLM Orchestration
• Generative AI Engineer
• AI Copilot Development
• Prompt Engineering

2 – Framework and Tooling – Mandatory – Weight 10 – Core Competencies:

• LangChain
• LangGraph
• Semantic Kernel
• AutoGen (Microsoft)
• CrewAI
• LlamaIndex
• OpenAI API / Azure OpenAI
• Hugging Face Transformers
• DSPy
• Instructor / Pydantic AI
• Guardrails AI
• LangSmith

3 – Data Science/Machine Learning – Mandatory – Weight 10 – Core Competencies:

• Deep Learning
• NLP (Natural Language Processing)
• Data Science
• Model training / evaluation
• Feature engineering
• Fine-tuning LLMs (LoRA / QLoRA)
• RLHF / alignment techniques
• Model benchmarking
• Transformer architecture

4 – Cloud Native architecture – Mandatory – Weight 10 – Core Competencies

• Python
• API development
• REST APIs
• Microservices
• FastAPI / Flask
• Async programming (asyncio)
• Docker
• CI/CD pipelines
• Event-driven architecture
• TypeScript (nice to have)

5 – RAG & Retrieval Systems – Mandatory – Weight 10 – Core Competencies

• RAG (Retrieval-Augmented Generation)
• Vector databases
• Embeddings
• Semantic search
• Hybrid search (BM25 + vector)
• Document chunking strategies
• Re-ranking (Cohere, cross-encoder)
• Knowledge graphs
• GraphRAG
• Contextual compression

6 – Vector DB and Search – Mandatory – Weight 10 – Core Competencies

• Pinecone
• FAISS
• ChromaDB
• Weaviate
• Azure AI Search
• Qdrant
• Milvus
• pgvector (PostgreSQL)

7 – Cloud Platform & DevOps – Mandatory – Weight 10 – Core Competencies

• Azure (Azure OpenAI, AI Foundry) OR AWS (Bedrock, SageMaker) OR GCP (Vertex AI)
• Docker
• Kubernetes
• Azure Container Apps
• GitHub Actions / Azure DevOps

8 – Observability & Evaluation – Mandatory – Weight 10 – Core Competencies

• LangSmith
• Weights & Biases
• Arize AI / Phoenix
• Prompt evaluation frameworks
• RAGAS (RAG evaluation)
• A/B testing of AI outputs
• Hallucination detection
• Tracing & distributed logging
• Eval-driven development

9 – AI Safety and Governance – Mandatory – Weight 10 – Core Competencies

• Responsible AI principles
• Prompt injection prevention
• Guardrails implementation
• Output filtering / content moderation
• Bias & fairness evaluation
• Model explainability (SHAP, LIME)
• Human-in-the-loop (HITL)
• EU AI Act / SDAIA NAII awareness
• Red-teaming LLMs

10 – General - – Optional – Weight 10 – Core Competencies

• GitHub with GenAI / agent projects
• Built chatbots / copilots in production
• Azure OpenAI / OpenAI APIs / Anthropic Claude
• Experience integrating tools into LLMs
• Production deployment experience
• Open-source contributions (LangChain etc.)
• Technical blog / papers on AI agents
• Conference talks or demos

Additional Requirements

- Fluent in English

- Strong communication and stakeholder skills

- Portfolio or GitHub projects preferred