Job Description Role :

Tech Stack: Python, Scikit Learn ,PyTorch ,LangChain , Postgress SQL, Transformers

Required Skills & Qualifications :

- Bachelor's or master's degree in computer science, Information Technology, or a related field.

- 8-10 years of experience in software development, with at least 3+ years in experience in AI/ML development

- Expertise in: Programming & ML Foundations

- Proficient in Python with extensive experience in major ML libraries, including TensorFlow, PyTorch, and Scikit-learn.

- Natural Language Processing: Skilled in leveraging NLP frameworks such as SpaCy, NLTK, and Hugging Face Transformers for text analysis and model development.

- Deep Learning Architectures: Strong understanding and practical application of CNNs, RNNs, LSTMs, and Transformer-based architectures.

- Generative & Agentic AI: Hands-on experience with Large Language Models (LLMs), LangChain, AutoGen, and Agentic AI frameworks with focus on memory management, vector search, embeddings, and Retrieval-Augmented Generation (RAG).

- Machine Learning Operations (MLOps): Knowledgeable in end-to-end ML lifecycle management using MLflow, DVC, and Kubeflow; experienced in implementing CI/CD pipelines for AI/ML workflows.

- Cloud & Deployment Expertise: Skilled in deploying AI solutions on AWS SageMaker, Azure AI, and GCP AI Platform; proficient with AWS services and cloud integration.

- Multi-Modal & Reinforcement Learning: Familiarity with multi-modal AI systems and reinforcement learning methodologies.

- DevOps : Working knowledge of DevOps principles; Any certification considered an added advantage.

- Excellent problem-solving, analytical, and communication skills.

Roles and Responsibilities :

- Develop and deploy AI/ML models across NLP, computer vision, and predictive analytics use cases.

- Work with state-of-the-art LLMs (OpenAI, Gemini, Mistral, Llama) and agent frameworks (LangChain, Lang graph, CrewAI, AutoGen).

- Design and Implementation of End to End -AI/ML -Architectural flows

- Implementation and Optimise Deep Learning Model (CNN,RNN, Transformers)

- Design and optimize conversational AI systems (chatbots, voice assistants) and recommendation engines.

- Build AI-powered APIs and microservices using FastAPI or Flask.

- Integrate vector databases (Pinecone, FAISS, Weaviate, Croma DB) for semantic search and RAG pipelines.

- Apply fine-tuning, prompt engineering, and evaluation techniques for improved model performance.

- Deploy and monitor AI solutions on cloud platforms (AWS, Azure, GCP) following MLOps best practices.

- Collaborate with cross-functional teams to bring AI solutions into production at scale.