AI/ML Developer

Role Overview

MidSenior AI/ML Developer

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:

  4–6 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