Senior Consultant

Key Responsibilities

Design, build, and deploy scalable ML/DL models for NLP, classification, recommendation, summarization, and text generation tasks.

Fine-tune Large Language Models (LLMs) using techniques such as LoRA, QLoRA, PEFT, and parameter-efficient training.

Build GenAI-powered solutions for RAG, document understanding, chatbot systems, intelligent agents, and decision-support tools.

Implement and orchestrate Agentic AI systems, including tool calling, memory, planning, reflection, and chaining workflows.

Develop robust APIs and microservices to expose AI/ML models to internal and external applications.

Integrate and manage vector databases (e.g., FAISS, Pinecone, Weaviate, Milvus) for semantic search, retrieval, and embedding stores.

Work with databases, including both SQL and NoSQL systems, to handle structured and unstructured data efficiently.

Utilize cloud platforms (AWS, Azure, GCP) for scalable model training, inference, and MLOps pipelines (e.g., SageMaker, Vertex AI, Azure ML).

Optimize model performance and cost through profiling, benchmarking, and cloud-native acceleration.

Collaborate with data scientists, product managers, and backend engineers to embed AI into real-world applications.

Stay current with AI research, tools, and community innovations (e.g., OpenAI, Hugging Face, Meta AI, etc.).

Required Qualifications

Bachelor’s or master’s degree in computer science, Artificial Intelligence, Data Science, or related field.

8-12 years of hands-on experience in machine learning and deep learning, including model deployment and tuning.

Proficient in Python and ML libraries such as PyTorch, TensorFlow, Scikit-learn.

Strong understanding of NLP techniques and experience with Transformer-based models.

Hands-on experience with Generative AI and Foundational Models (e.g., GPT, Claude, LLaMA, Mistral, PaLM).

Experience with API development using FastAPI, Flask, or similar frameworks.

Familiarity with SQL and NoSQL databases, understanding their role in supporting AI/ML pipelines and real-time applications.

Experience with vector databases for semantic search and LLM applications.

Cloud experience with at least one major platform (AWS preferred, Azure, or GCP).

Familiarity with containerization (Docker), version control (Git), and ML lifecycle tools (e.g., MLflow, DVC).

Exposure to Agentic AI systems, autonomous agents, and tool use frameworks such as LangChain or Autogen.

Senior Consultant