01Overview
Role Summary
Design, develop, deploy, and optimise enterprise AI/ML, Deep Learning, Computer Vision, and Generative AI solutions. Build scalable AI platforms, LLM applications, RAG systems, Agentic AI workflows, AI chatbots/copilots, and intelligent automation while ensuring performance, security, scalability, and production readiness.
Candidates must have hands-on experience with at least one cloud platform: AWS, Microsoft Azure, or Google Cloud Platform (GCP), preferably in deploying or managing AI/ML or Generative AI solutions in production.
Position: Lead AI/ML Engineer
Experience: 7+ Years
Location: Indore
Employment Type: Full-Time
02Responsibilities
- Develop ML, Deep Learning, Computer Vision, and Generative AI applications.
- Build LLM, RAG, AI chatbot/copilot, and Agentic AI solutions using LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, and MCP.
- Fine-tune, evaluate, optimise, and deploy foundation models using LoRA/QLoRA, PEFT, Hugging Face, Ollama, vLLM, and TensorRT-LLM.
- Design scalable AI architectures, REST APIs, FastAPI/Flask microservices, Docker/Kubernetes deployments, and cloud-native solutions.
- Deploy and manage AI/ML and GenAI applications on AWS, Azure, or GCP, ensuring scalability, security, reliability, and cost optimisation.
- Develop Computer Vision, OCR, Intelligent Document Processing (IDP), object detection, segmentation, and document intelligence systems.
- Build model training, inference, CI/CD, MLOps, monitoring, benchmarking, A/B testing, and production pipelines.
- Collaborate with Product, Data Engineering, DevOps, and MLOps teams; mentor engineers and drive technical best practices.
03What We're Looking For
- Programming: Python, SQL, Git, REST APIs
- AI/ML: Supervised and Unsupervised Learning, Feature Engineering, Model Evaluation, Explainability, Time Series, Recommendation Systems
- Deep Learning: TensorFlow, PyTorch, Hugging Face, CNN, RNN/LSTM/GRU, Transformers, Transfer Learning, Fine-tuning, Quantization
- Generative AI: LLMs, Prompt Engineering, Embeddings, Function Calling, Context Engineering, Guardrails, AI Safety
- RAG & Agentic AI: RAG, LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, MCP, AI Agents, Multi-Agent Systems, Tool Calling, Vector and Semantic Search
- Computer Vision: OpenCV, YOLO, Detectron2, OCR (PaddleOCR, EasyOCR, Tesseract), Object Detection, Image Classification and Segmentation, Intelligent Document Processing (IDP)
- Backend & APIs: FastAPI, Flask, Microservices, JWT/OAuth, Async APIs
- Databases: PostgreSQL, MySQL, MongoDB, Redis, Pinecone, ChromaDB, FAISS, Weaviate, Milvus
- Mandatory: Hands-on experience with at least one of the following:
- AWS: Amazon Bedrock, SageMaker, ECS/EKS, Lambda, S3
- Microsoft Azure: Azure OpenAI, Azure Machine Learning, Azure Kubernetes Service (AKS), Azure AI services
- Google Cloud Platform (GCP): Vertex AI, Cloud Run, BigQuery, Google Kubernetes Engine (GKE)
- Cloud Platforms —
- Mandatory: Hands-on experience with at least one of the following:
- AWS: Amazon Bedrock, SageMaker, ECS/EKS, Lambda, S3Microsoft
- Azure: Azure OpenAI, Azure Machine Learning, Azure Kubernetes Service (AKS), Azure AI services
- Google Cloud Platform (GCP): Vertex AI, Cloud Run, BigQuery, Google Kubernetes Engine (GKE)
04What We Offer
- Soft Skills
- Strong analytical and problem-solving skills, AI system design, performance optimisation, debugging, stakeholder communication, mentoring, documentation, and Agile/Scrum collaboration.
