AI/ML Developer

Role Overview

Key Responsibilities

AI/ML & GenAI Development

• Design, train, and deploy ML/DL models and GenAI solutions (LLMs, diffusion models, transformers, multimodal systems).
  
• Fine-tune and optimize open-source models (LLaMA, Falcon, Mistral, BERT, T5 etc) and integrate with closed-source APIs (OpenAI GPT, Anthropic Claude, Google Gemini, IBM WatsonX, Azure OpenAI etc).
  
• Build computer vision pipelines (object detection, OCR, image/video analytics).
  

Agentic AI & Orchestration

• Develop Agentic AI systems capable of multi-step reasoning, planning, and autonomous task execution.
  
• Work with LangChain, LlamaIndex, Haystack and similar frameworks to build tool-using, retrieval-augmented, and multi-agent systems.
  
• Implement and integrate MCP (Model Context Protocol) for model interoperability, context-sharing, and agent-to-agent collaboration.
  
• Build AI Agents for conversational, voice, and avatar use cases leveraging VAPI, Voiceflow, Retell, HeyGen, Synthesia.
  
• Orchestrate workflows using n8n, Airflow, LangGraph, and enterprise workflow engines.
  

Backend APIs & Deployment

• Expose AI models as production-grade REST APIs using FastAPI, Flask, or Django REST Framework.
  
• Build microservices-based AI architectures and integrate with enterprise applications.
  
• Manage server deployments, Docker containers, and Kubernetes  clusters.
  
• Optimize deployments on either AWS SageMaker, Azure ML, GCP Vertex AI etc. or scalability, cost-efficiency, and compliance.
  

MLOps & LLMOps

• Implement CI/CD pipelines, MLflow/Weights & Biases, and GitOps for experiment tracking and versioning.
  
• Deploy LLMOps frameworks for prompt management, hallucination monitoring, and safety guardrails.
  
• Monitor drift detection, retraining workflows, and responsible AI compliance.
  

Client-Facing & AI Enablement

• Work directly with clients and stakeholders to identify opportunities, define requirements, and plan AI/GenAI roadmaps.
  
• Conduct AI enablement workshops, PoCs, and use-case discovery sessions.
  
• Translate high-level business problems into AI-driven workflows and agent-based solutions.
  
• Work in an Agile/Scrum environment, ensuring iterative delivery with continuous feedback.
  
• Communicate complex AI concepts effectively to both technical and non-technical audiences.
  

Required Skills & Qualifications

• Experience: 36 years in AI/ML development, with at least 2+ years in Generative AI and enterprise deployments.
  
• Programming: Strong Python (NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, Hugging Face).
  
• Models: Experience with both open-source (Falcon, LLaMA, Mistral, etc.) and closed-source APIs (OpenAI, Anthropic, Gemini, WatsonX, Azure OpenAI).
  
• Agentic AI: Hands-on with LangChain, LlamaIndex, LangGraph, and multi-agent frameworks.
  
• MCP: Familiarity with Model Context Protocol (MCP) for model/agent interoperability.
  
• Backend & APIs: Strong experience in FastAPI, Flask, or Django REST for production-ready model APIs.
  
• Cloud & Deployment: Skilled in Docker, Kubernetes, and cloud ML services (AWS SageMaker, Azure ML, GCP Vertex AI).
  
• Applied Use Cases: Sales forecasting, churn prediction, predictive scheduling, and business analytics.
  
• Databases: SQL/NoSQL + vector databases (Pinecone, FAISS, Milvus, Weaviate).
  
• Workflow Tools: n8n, Airflow, LangChain-based orchestration.
  
• Communication: Excellent verbal, written, and client-facing presentation skills.
  
• Agile Mindset: Experience delivering solutions in Agile/Scrum environments.
  

Preferred Qualifications

• Exposure to multimodal AI (text + vision + audio).
  
• Knowledge of enterprise AI enablement (security, compliance, governance).
  
• Experience building agent-driven business solutions (voice agents, digital avatars, RAG-powered copilots).
  
• Contributions to open-source AI frameworks.
  
• Familiarity with ethical AI frameworks (bias detection, explainability, compliance).
  
• Nice to have is an experience real-world business use cases like:
  
• Sales forecasting and demand prediction
  
• Customer churn modeling and retention strategies
  
• Scheduling optimizers & workforce planning
  
• Predictive analytics for business KPIs