AI Consultant

• Computer vision: Convolutional neural networks, recurrent neural networks, object detection
• Robotics: Reinforcement learning, motion planning, control systems

Job Description: AI Consultant

We are seeking an accomplished Generative AI Consultant to drive the design and implementation of innovative AI solutions for our clients. The Generative AI Consultant will play a critical role in understanding client needs, designing tailored solutions, and ensuring the successful delivery of projects that meet defined metrics. This role requires strong technical expertise across Generative and Agentic AI—including LLMs, retrieval-augmented generation (RAG), autonomous and multi-agent systems, and modern interoperability standards such as the Model Context Protocol (MCP)—coupled with excellent communication skills to engage with clients and internal teams effectively.

Primary Skill Set

• Generative AI Expertise: Good understanding of modern Generative AI techniques and foundation models, including transformer-based Large Language Models (LLMs), diffusion models, and multimodal models, as well as earlier architectures such as GANs and VAEs. Proven experience in applying these techniques to real-world problems for tasks such as text, code, image, and multimodal generation. Conversant with modern Gen AI development techniques and tooling such as advanced prompt engineering, structured outputs, function/tool calling, and orchestration frameworks like LangChain, LangGraph, LlamaIndex, and Semantic Kernel. Hands-on exposure to both API-based (e.g., Claude, GPT, Gemini) and open-source (e.g., Llama, Mistral) LLM-based solution design.

• Agentic AI & Orchestration: Hands-on experience designing autonomous and multi-agent systems that reason, plan, and act using tools. Familiarity with agentic design patterns (e.g., ReAct, planning, reflection, tool use, human-in-the-loop) and agent frameworks such as LangGraph, CrewAI, MAF, the OpenAI Agents SDK, and Google’s Agent Development Kit (ADK). Experience building agentic workflows with memory, state management, and reliable multi-step task execution.
• Model Context Protocol (MCP) & Interoperability: Practical understanding of the Model Context Protocol (MCP) for standardized, secure connectivity between LLMs/agents and external tools, data sources, and systems. Ability to build and consume MCP servers and clients, and to work with MCP primitives such as tools, resources, and prompts. Awareness of related interoperability standards (e.g., agent-to-agent communication) for composing enterprise-grade agentic systems.
• Agent Skills & Extensibility: Experience extending agent capabilities through modular, reusable skills—packaged instructions, scripts, and resources (e.g., SKILL.md-style capability modules) that agents load on demand via progressive disclosure. Ability to design custom tools, connectors, and skills that let agents perform specialized, domain-specific tasks reliably and safely.
• Retrieval-Augmented Generation (RAG) & Knowledge Systems: Proven experience designing RAG and knowledge-grounded systems, including chunking strategies, embeddings, vector databases (e.g., Pinecone, Weaviate, Chroma, pgvector, FAISS), hybrid search, reranking, and evaluation of retrieval quality. Familiarity with advanced patterns such as GraphRAG and agentic RAG to reduce hallucination and improve factual grounding.
• Technical Proficiency: An overall understanding of below technologies is required :
  • Machine learning algorithms: Linear regression, logistic regression, decision trees, random forests, support vector machines, neural networks
  • Data science tools: NumPy, SciPy, Pandas, Matplotlib, TensorFlow, Keras
  • Cloud computing platforms: AWS, Azure, GCP
  • Natural language processing (NLP): Transformer models, attention mechanisms, word embeddings
  • Computer vision: Convolutional neural networks, recurrent neural networks, object detection
  • Robotics: Reinforcement learning, motion planning, control systems
  • Data ethics: Bias in machine learning, fairness in algorithms
  • Foundation models & LLMs: GPT, Claude, Gemini, Llama, Mistral; multimodal and reasoning models; context windows, tokenization, and fine-tuning (LoRA/PEFT), RLHF/RLAIF concepts
  • LLM application & agent frameworks: LangChain, LangGraph, LlamaIndex, Semantic Kernel, Haystack, CrewAI, AutoGen
  • Interoperability & integration: Model Context Protocol (MCP), function/tool calling, structured outputs, API integration, event-driven and orchestration patterns
  • Cloud AI platforms & model hosting: Amazon Bedrock, Azure OpenAI / AI Foundry, Google Vertex AI, Hugging Face
  • Vector databases & retrieval: Pinecone, Weaviate, Chroma, pgvector, FAISS; embeddings, semantic and hybrid search, reranking
  • MLOps / LLMOps & deployment: Docker, Kubernetes, FastAPI, CI/CD; o