Job Summary
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
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Design and develop AI agents and multi-agent systems
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Build RAG pipelines
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Develop scalable AI services using APIs and microservices
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Integrate LLMs with enterprise systems
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Apply Responsible AI practices
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Engage with stakeholders and present solutions
Required Experience & Qualifications
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2–6 years of experience in AI engineering or ML
Key Skills & Competencies
1 - Agentic AI & LLM Eng – Mandatory – Weight 10 – Core Competencies:
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AI Agents
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Autonomous Agents
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Multi-agent systems
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LLM Applications
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LLM Orchestration
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Generative AI Engineer
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AI Copilot Development
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Prompt Engineering
2 – Framework and Tooling – Mandatory – Weight 10 – Core Competencies:
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LangChain
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LangGraph
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Semantic Kernel
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AutoGen (Microsoft)
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CrewAI
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LlamaIndex
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OpenAI API / Azure OpenAI
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Hugging Face Transformers
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DSPy
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Instructor / Pydantic AI
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Guardrails AI
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LangSmith
3 – Data Science/Machine Learning – Mandatory – Weight 10 – Core Competencies:
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Deep Learning
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NLP (Natural Language Processing)
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Data Science
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Model training / evaluation
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Feature engineering
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Fine-tuning LLMs (LoRA / QLoRA)
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RLHF / alignment techniques
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Model benchmarking
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Transformer architecture
4 – Cloud Native architecture – Mandatory – Weight 10 – Core Competencies
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Python
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API development
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REST APIs
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Microservices
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FastAPI / Flask
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Async programming (asyncio)
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Docker
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CI/CD pipelines
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Event-driven architecture
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TypeScript (nice to have)
5 – RAG & Retrieval Systems – Mandatory – Weight 10 – Core Competencies
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RAG (Retrieval-Augmented Generation)
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Vector databases
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Embeddings
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Semantic search
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Hybrid search (BM25 + vector)
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Document chunking strategies
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Re-ranking (Cohere, cross-encoder)
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Knowledge graphs
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GraphRAG
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Contextual compression
6 – Vector DB and Search – Mandatory – Weight 10 – Core Competencies
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Pinecone
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FAISS
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ChromaDB
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Weaviate
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Azure AI Search
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Qdrant
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Milvus
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pgvector (PostgreSQL)
7 – Cloud Platform & DevOps – Mandatory – Weight 10 – Core Competencies
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Azure (Azure OpenAI, AI Foundry) OR AWS (Bedrock, SageMaker) OR GCP (Vertex AI)
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Docker
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Kubernetes
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Azure Container Apps
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GitHub Actions / Azure DevOps
8 – Observability & Evaluation – Mandatory – Weight 10 – Core Competencies
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LangSmith
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Weights & Biases
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Arize AI / Phoenix
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Prompt evaluation frameworks
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RAGAS (RAG evaluation)
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A/B testing of AI outputs
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Hallucination detection
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Tracing & distributed logging
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Eval-driven development
9 – AI Safety and Governance – Mandatory – Weight 10 – Core Competencies
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Responsible AI principles
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Prompt injection prevention
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Guardrails implementation
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Output filtering / content moderation
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Bias & fairness evaluation
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Model explainability (SHAP, LIME)
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Human-in-the-loop (HITL)
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EU AI Act / SDAIA NAII awareness
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Red-teaming LLMs
10 – General -
– Optional – Weight 10 – Core Competencies
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GitHub with GenAI / agent projects
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Built chatbots / copilots in production
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Azure OpenAI / OpenAI APIs / Anthropic Claude
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Experience integrating tools into LLMs
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Production deployment experience
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Open-source contributions (LangChain etc.)
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Technical blog / papers on AI agents
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Conference talks or demos
Additional Requirements
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Fluent in English
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Strong communication and stakeholder skills
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Portfolio or GitHub projects preferred