AI Engineer

Roles And Responsibilities

• AI Engagements: Independently manage end-to-end delivery of AI-led transformation projects across industries, ensuring value realization and high client satisfaction.
• Strategic Consulting & Roadmapping: Identify key enterprise challenges and translate them into AI solution opportunities, crafting transformation roadmaps that leverage RAG, LLMs, and intelligent agent frameworks.
• LLM/RAG Solution Design & Implementation: Architect and deliver cutting-edge AI systems using Python, LangChain, LlamaIndex, OpenAI function calling, semantic search, and vector store integrations (FAISS, Qdrant, Pinecone, ChromaDB).
• Agentic Systems: Design and deploy multi-step agent workflows using frameworks like CrewAI, LangGraph, AutoGen or ReAct, optimizing tool-augmented reasoning pipelines.
• Client Engagement & Advisory: Build lasting client relationships as a trusted AI advisor, delivering technical insight and strategic direction on generative AI initiatives.
• Hands-on Prototyping: Rapidly prototype PoCs using Python and modern ML/LLM stacks to demonstrate feasibility and business impact.
• Thought Leadership: Conduct market research, stay updated with the latest in GenAI and RAG/Agentic systems, and contribute to whitepapers, blogs, and new offerings.

Essential Skills

• Leadership Quality: Proven track record in leading cross-functional teams and delivering enterprise-grade AI projects with tangible business impact.
• Business Consulting Mindset: Strong problem-solving, stakeholder communication, and business analysis skills to bridge technical and business domains.
• Python & AI Proficiency: Advanced proficiency in Python and popular AI/ML libraries (e.g., scikit-learn, PyTorch, TensorFlow, spaCy, NLTK). Solid understanding of NLP, embeddings, semantic search, and transformer models.
• LLM Ecosystem Fluency: Experience with OpenAI, Cohere, Hugging Face models; prompt engineering; tool/function calling; and structured task orchestration.
• Independent Contributor: Ability to own initiatives end-to-end, take decisions independently, and operate in fast-paced environments.
• Education: Bachelor's or Master’s in Computer Science, AI, Engineering, or related field.
• Experience: Minimum 5 years of experience in consulting or technology roles, with at least 3 years focused on AI & ML solutions.

Preferred Skills

• Cloud Platform Expertise: Strong familiarity with Microsoft Azure (preferred), AWS, or GCP — including compute instances, storage, managed services, and serverless/cloud-native deployment models.
• Programming Paradigms: Hands-on experience with both functional and object-oriented programming in AI system design.
• Hugging Face Ecosystem: Proficiency in using Hugging Face Transformers, Datasets, and Model Hub.
• Vector Store Experience: Hands-on experience with FAISS, Qdrant, Pinecone, ChromaDB.
• LangChain Expertise: Strong proficiency in LangChain for agentic task orchestration and RAG pipelines.
• MLOps & Deployment: CI/CD for ML pipelines, MLOps tools (MLflow, Azure ML), containerization (Docker/Kubernetes).
• Cloud & Service Architecture: Knowledge of microservices, scaling strategies, inter-service communication.
• Programming Languages: Proficiency in Python and C# for enterprise-grade AI solution development.

Additional Skills

• Excellent customer interfacing and stakeholder engagement skills.
• Strong verbal and written communication in business and technical contexts.
• High attention to detail and structured problem-solving.
• Passion for AI ethics, safety, performance, and optimization in enterprise-grade systems.

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[email protected]