Deep Learning Lead

Role: Hands-on Technical Lead - AI / Deep Learning / Generative AI

Role Profile

- We are looking for a highly skilled and hands-on Deep Learning Lead with 4-6 years of experience to lead the design, development, optimization, and deployment of advanced Deep Learning, Generative AI, LLM, RAG, Agentic AI, NLP, Computer Vision, and Multimodal AI solutions.
- The ideal candidate should have strong fundamentals in Deep Learning and Machine Learning, excellent programming skills, and proven experience taking AI models from research/prototype to production.

Responsibilities

- Lead the design, development, and deployment of production-grade Deep Learning and AI solutions.
- Design and implement advanced neural network architectures using PyTorch, TensorFlow, Transformers, and related frameworks.
- Develop and optimize AI/ML models for real-world enterprise applications.
- Work extensively with Large Language Models (LLMs), Generative AI, Transformer architectures, embeddings, and foundation models.
- Build LLM-powered applications using both proprietary and open-source models such as GPT, Claude, Gemini, Llama, Mistral, Qwen, DeepSeek, and equivalent models.
- Design and implement production-grade Retrieval-Augmented Generation (RAG) pipelines.
- Develop advanced RAG solutions involving semantic search, hybrid search, reranking, query expansion, contextual retrieval, Agentic RAG, and GraphRAG.
- Work with vector databases such as Pinecone, Qdrant, Weaviate, Milvus, FAISS, ChromaDB, pgvector, or equivalent.
- Design productive document ingestion, parsing, chunking, embedding, indexing, retrieval, and context-generation pipelines.
- Develop and optimize AI Agents and Agentic AI workflows involving reasoning, planning, memory, tool calling, and autonomous task execution.
- Build multi-agent systems and orchestration workflows using frameworks such as LangGraph, LangChain, LlamaIndex, AutoGen, CrewAI, Semantic Kernel, or equivalent.
- Work with Model Context Protocol (MCP) and other emerging agent interoperability technologies.
- Implement prompt engineering, context engineering, structured outputs, function calling, tool use, and guardrails.
- Perform LLM fine-tuning, instruction tuning, LoRA, QLoRA, PEFT, and other parameter-efficient optimization techniques.
- Work on model quantization, pruning, distillation, compression, and inference optimization.
- Build robust AI evaluation frameworks to measure accuracy, relevance, groundedness, hallucination, latency, reliability, cost, and overall model performance.
- Conduct model benchmarking, A/B testing, experimentation, error analysis, and continuous model improvement.
- Work on multimodal AI systems involving text, image, audio, video, speech, and vision-language models.




- Develop and optimize Computer Vision, NLP, Speech AI, OCR, VLM, and multimodal intelligence solutions where required.
- Design scalable AI inference and model-serving architectures for high-performance production environments.
- Optimize GPU utilization, inference latency, throughput, memory consumption, and deployment costs.
- Work with technologies such as CUDA, vLLM, TensorRT, Triton Inference Server, ONNX, OpenVINO, or equivalent.
- Build and maintain production-grade MLOps pipelines covering experimentation, model versioning, deployment, monitoring, and continuous improvement.
- Deploy AI workloads across AWS, Azure, GCP, private cloud, on-premise, and edge environments.
- Work with Docker, Kubernetes, MLflow, CI/CD, model registries, monitoring, logging, and observability platforms.
- Develop scalable AI services and APIs using Python, FastAPI, Flask, or equivalent technologies.
- Ensure AI systems meet requirements related to performance, scalability, security, reliability, privacy, and responsible AI.
- Implement AI safety mechanisms including guardrails, prompt injection protection, hallucination mitigation, content filtering, and model security practices.
- Research emerging developments in LLMs, Generative AI, Agentic AI, Multimodal AI, RAG, foundation models, and Deep Learning and translate relevant advancements into production capabilities.
- Read and implement relevant research papers and state-of-the-art AI techniques.
- Lead technical architecture discussions, design reviews, code reviews, and AI engineering best practices.
- Mentor AI/ML engineers and provide technical guidance on complex ML and Deep Learning problems.
- Collaborate with Product, Engineering, Data, DevOps, and Business teams to translate requirements into scalable AI solutions.
- Troubleshoot complex model, data, inference, performance, and production issues.
- Drive AI initiatives from problem definition data model development evaluation deployment monitoring continuous improvement.

Required Qualifications : -

- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Mathematics, Electronics, or a related technical field.
- 4-6 years of hands-on professional experience in Deep Learning, Machine Learning, AI Engineering, or related areas.
- Strong programming experience in Python.




- Strong hands-on experience with PyTorch; TensorFlow experience is an advantage.
- Strong understanding of Deep Learning fundamentals, neural networks, CNNs, RNNs, LSTMs, GRUs, Transformers, Attention mechanisms, optimization, and model evaluation.
- Strong understanding of Machine Learning algorithms, statistics, probability, linear algebra, and optimization techniques.
- Hands-on experience building and deploying LLM / Generative AI applications.
- Strong practical experience with RAG, embeddings, vector databases, semantic search, and retrieval pipelines.
- Experience with LLM fine-tuning, LoRA/QLoRA, PEFT, prompt engineering, and LLM evaluation.
- Experience building AI Agents, tool-calling workflows, or Agentic AI systems.
- Experience with at least one AI orchestration framework such as LangChain, LangGraph, LlamaIndex, AutoGen, CrewAI, or Semantic Kernel.
- Experience with Docker, Git, APIs, cloud platforms, and production deployment.
- Understanding of MLOps, model serving, monitoring, CI/CD, and scalable AI infrastructure.
- Ability to independently own complex AI projects from conception through production deployment.

Preferred Skills : -

- Experience with Multimodal AI, Vision-Language Models, Computer Vision, NLP, Speech AI, OCR, or real-time AI systems.
- Experience with GraphRAG, Knowledge Graphs, Neo4j, or hybrid retrieval architectures.
- Experience with MCP, multi-agent architectures, agent memory, and autonomous workflows.
- Experience with vLLM, TensorRT, Triton, ONNX, OpenVINO, CUDA, or GPU optimization.
- Knowledge of RLHF, RLAIF, reinforcement learning, synthetic data generation, and model distillation.
- Experience with Kubernetes, distributed training, distributed inference, and GPU orchestration.
- Familiarity with LLM observability and evaluation tools such as LangSmith, Langfuse, Arize, Phoenix, or equivalent.
- Experience working with AWS, Azure, GCP, private cloud, or on-premise AI infrastructure.
- Understanding of AI security, responsible AI, model safety, guardrails, and privacy.
- Strong research mindset with the ability to understand and implement recent AI research.
- Experience working in a deep-tech, product-focused, or enterprise AI environment.

Key Skills : -

- Deep Learning , Machine Learning , Python , PyTorch , Transformers , LLMs , Generative AI , RAG , Advanced RAG , Agentic RAG , AI Agents , Multi-Agent Systems , Embeddings , Vector Databases , LLM Fine-Tuning , LoRA , QLoRA , PEFT , Prompt Engineering , Context Engineering , LLM Evaluation , LangChain , LangGraph , LlamaIndex , MCP , Multimodal AI , Computer Vision , NLP , VLM , MLOps , Docker , Kubernetes , FastAPI , CUDA , vLLM , TensorRT , ONNX , OpenVINO , Cloud , GPU Optimization"

📌 Deep Learning Lead (Hyderabad)
🏢 TensorGo
📍 Hyderabad