Description

Role : Lead AI/ML Developer

Experience: 8-11 Years

Location: Bengaluru

Job Description

Role : Lead AI/ML Developer

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

Required Skills & Qualifications

Bachelors or masters degree in computer science, Information Technology , or a related field.
8-11 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.

Notice period: Candidate has to join within a month (Mandatorily)

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