Senior AI/ML Consultant

Audax Labs, LLC.; Dadri; Hybrid; INR 4,000,000 - 7,000,000; Full time

Job summary

Audax Labs, LLC. is seeking an experienced AI Lead to drive design, development, and deployment of advanced ML solutions with a focus on NLP, LLMs, and Generative AI. You will lead a team to build AI agents and scalable architectures.

The role emphasizes hands-on delivery, MCP server integration, and cloud deployment across Azure, GCP, and AWS, while mentoring junior engineers and advancing MLOps practices within the organization.

Qualifications

• 7+ years hands-on ML/AI solution development, with NLP/LLMs focus.
• Expertise in designing AI agents, multi-agent systems, and RAG apps.
• Strong knowledge of MCP servers, tool integrations, and secure AI architectures.

Responsibilities

• Lead end-to-end AI/ML solution development focused on NLP and Generative AI.
• Design and deploy AI Agents and multi-agent systems to automate workflows.
• Architect MCP servers and integration layers for secure connectivity.
• Leverage LangChain, LangGraph, LlamaIndex, and related frameworks.
• Oversee data engineering and model deployment on Azure/GCP/AWS.
• Mentor engineers and drive best practices in MLOps.

Skills

NLP

LLMs

Generative AI

AI Agents

Agentic AI

Prompt engineering

MLOps

Cloud platforms (Azure, GCP, AWS)

System architecture

Tools

LangChain

LangGraph

LlamaIndex

CrewAI

AutoGen

Llama

PyTorch

TensorFlow

Job description

Experienced and visionary AI Lead to drive the design, development, and deployment of advanced machine learning solutions. The ideal candidate will have hands‑on expertise in NLP, LLMs, and Generative AI, combined with strong leadership skills to guide a team in delivering impactful AI‑driven innovations.

Key Responsibilities:

• Lead the end‑to‑end development of AI/ML solutions, with a focus on NLP, LLMs, and Generative AI applications.
• Design, develop, and deploy AI Agents and multi‑agent systems to automate business workflows and decision‑making processes.
• Architect and implement Model Context Protocol (MCP) servers and AI integration layers to enable secure and scalable connectivity between LLMs, enterprise applications, databases, APIs, and external tools.
• Drive innovation by leveraging frameworks such as LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, PyTorch, and TensorFlow.
• Architect and optimize machine learning pipelines, ensuring scalability, performance, and reliability.
• Collaborate with cross‑functional teams to translate business objectives into AI‑driven solutions.
• Oversee data engineering and model deployment on cloud platforms (Azure, GCP, AWS).
• Ensure robust API integration and seamless interaction between AI models, MCP servers, enterprise systems, and business applications.
• Monitor and continuously improve model performance, implementing best practices for MLOps.
• Mentor junior engineers and foster a culture of technical excellence and innovation.

Key Requirements:

• Experience: 7+ years of hands‑on experience in machine learning and AI solution development, with a strong focus on NLP, LLMs, Generative AI, AI Agents, and Agentic AI architectures.
• Generative AI: Practical experience with LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, Llama, or similar frameworks.
• Hands‑on experience designing and building AI Agents, multi‑agent systems, RAG (Retrieval‑Augmented Generation) applications, and enterprise AI assistants.
• Experience developing and deploying Model Context Protocol (MCP) servers, tool integrations, function calling frameworks, and agent orchestration platforms.
• Strong understanding of prompt engineering, vector databases, embeddings, semantic search, and AI workflow orchestration.
• Experience integrating LLMs with enterprise applications, APIs, databases, workflow engines, and business systems through secure and scalable architectures.
• Strong understanding of cloud platforms (Azure, GCP, AWS) and API development.

Preferred Qualifications:

• Experience building enterprise‑grade Agentic AI solutions, AI copilots, autonomous workflow agents, and knowledge assistants.
• Experience with MCP‑based architectures and integration of AI systems with enterprise tools such as CRM, ERP, HRMS, ITSM, document management, and workflow platforms.
• Knowledge of RAG frameworks, vector databases, and knowledge graph technologies.
• Experience implementing AI governance, guardrails, observability, security controls, and responsible AI practices for agent‑based systems.
• Exposure to Azure OpenAI, AWS Bedrock, Google Vertex AI, Azure AI Foundry, or comparable enterprise AI platforms.