Senior Data Scientist

Senior

Ahmedabad (full-time)

6+ years

Tecblic,

Key Responsibilities

• AI Research & Innovation: Drive R&D in Generative AI, Large Language Models (LLMs), and Agentic AI, exploring new architectures, algorithms, and frameworks.
• End-to-End ML Lifecycle Ownership: Lead the design, development, training, fine-tuning, and deployment of ML/AI systems at scale.
• Model Optimization & Scaling: Optimize pre-trained LLMs (GPT, LLaMA, T5, etc.) for domain-specific use cases, focusing on performance, scalability, and cost efficiency.
• GenAI Applications: Architect solutions using text-to-X (text, code, image, audio) generation, prompt engineering, retrieval-augmented generation (RAG), and fine-tuning techniques.
• Agentic AI Systems: Design and implement autonomous agent frameworks for reasoning, decision-making, and multi-step task execution.
• Data Engineering & Pipelines: Oversee advanced data preprocessing, pipeline automation, and scalable data workflows.
• MLOps & Deployment: Build production-grade ML pipelines using Docker, Kubernetes, MLflow, or Kubeflow, with monitoring and governance for reliability.
• Mentorship & Leadership: Provide technical leadership, mentor junior engineers, and collaborate with cross-functional stakeholders (engineering, product, business).
• Experimentation: Lead prototyping in reinforcement learning, generative models (GANs, VAEs), vector search (FAISS, Pinecone, Weaviate), and hybrid AI approaches.

Requirements

Core Technical Skills

• Programming: Advanced proficiency in Python (ML, DS, automation) and exposure to C++/Java/Go (optional).
• ML Frameworks: Deep expertise in PyTorch, TensorFlow, Hugging Face, Scikit-learn, LangChain, or similar.
• LLMs & NLP: Strong knowledge of transformer-based models (GPT, BERT, Bloom, LLaMA) and NLP tasks (summarization, QA, entity recognition, semantic search, text classification).
• GenAI Expertise: Hands-on experience with RAG, prompt engineering, fine-tuning, few-shot learning, and multimodal GenAI systems.
• Agentic AI: Practical knowledge of autonomous agents, decision-making systems, and orchestration frameworks.
• Data Engineering: Strong command of Pandas, NumPy, SQL, and experience with distributed systems (Spark, Dask).
• Cloud & Deployment: Proficiency in AWS/GCP/Azure, with hands-on experience in containerization (Docker), orchestration (Kubernetes), and serverless AI deployment.

Additional Skills (Nice to Have)

• MLOps expertise: MLflow, Kubeflow, Vertex AI, SageMaker.
• Experience with vector databases (FAISS, Pinecone, Weaviate, Milvus).
• Advanced reinforcement learning, generative models (GANs/VAEs), or multimodal AI systems (vision, speech, text).
• Strong background in algorithms, data structures, statistics, linear algebra, and probability.

General & Soft Skills

• Strategic mindset with the ability to architect enterprise-grade AI solutions.
• Strong problem-solving and critical-thinking skills.
• Excellent leadership, mentorship, and cross-team collaboration abilities.
• Passion for continuous learning and keeping pace with emerging AI trends.