Artificial Intelligence Consultant

Description

Role : Lead AI/ML Developer

Experience: 8-11 Years

Location: Bengaluru

Role

Tech Stack

Required Skills & Qualifications

• Bachelor's or master's 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)