Company Brief :

One of our key employers is a leading technology solutions and software development company specializing in enterprise applications, digital transformation, cloud computing, AI/ML, data engineering, and custom software development. The organization delivers innovative, scalable, and high-quality solutions to clients across various industries, helping businesses accelerate their digital initiatives. It fosters a team-oriented, innovation-driven work culture with excellent opportunities for professional growth, continuous learning, and exposure to modern technologies.

Key Responsibilities:

AI Strategy & Technical Leadership

- Lead the architecture, design, and implementation of enterprise-scale AI/ML solutions.
- Define and drive the AI/ML roadmap, ensuring alignment with business objectives and product strategy.
- Provide technical leadership and mentorship to AI/ML engineers and data scientists.
- Establish best practices for AI model development, experimentation, deployment, and monitoring. Generative AI & LLM Systems
- Design and develop Generative AI applications using LLMs such as GPT, LLaMA, Gemini, or custom models.
- Architect and implement Retrieval-Augmented Generation (RAG) pipelines for enterprise knowledge systems.
- Lead initiatives for LLM fine-tuning, prompt engineering, and model optimization.
- Design AI agent architectures using frameworks like LangChain, LangGraph, and LlamaIndex.

AI/ML Model Development:

- Develop and deploy NLP, Computer Vision, and multimodal AI models for real-world business applications.
- Implement advanced deep learning architectures using PyTorch, TensorFlow, or Keras.
- Identify and evaluate pre-trained and foundation models suitable for specific use cases.




- Drive data preprocessing, feature engineering, and dataset curation for model training. AI Platform & Infrastructure

• Design scalable AI infrastructure and MLOps pipelines for model training, deployment, and monitoring.
- Deploy AI solutions across cloud platforms (AWS, Azure, GCP) or hybrid/on-premise environments.
- Build APIs, microservices, and pipelines to integrate AI capabilities into enterprise applications.
- Lead efforts in model optimization, inference acceleration, and resource efficiency. Performance Optimization & Quality
- Conduct model evaluation, benchmarking, and continuous performance optimization.

• Optimize AI systems for latency, scalability, and cost efficiency.
- Implement testing, monitoring, and observability frameworks for AI systems in production. Collaboration & Innovation
- Work closely with Product, Engineering, and Data teams to define AI-powered product features.
- Stay at the forefront of AI research and emerging technologies, evaluating their business impact.
- Promote a culture of experimentation, innovation, and knowledge sharing within the AI team.

Required Skills & Experience AI & Machine Learning

- 610 years of experience in AI/ML development and deployment.
- Strong expertise in supervised and unsupervised learning techniques, including regression, classification,



clustering, SVMs, and neural networks. Generative AI & LLMs
- Hands-on experience with LLM training, fine-tuning, prompt engineering, and optimization.
- Experience building GenAI applications such as chatbots, AI assistants, and document intelligence systems. NLP & Computer Vision • Strong experience in Natural Language Processing and Computer Vision.
- Hands-on expertise with Transformers, OpenCV, YOLO, and R-CNN architecture. AI Agents & Frameworks
- Experience with multi-agent frameworks such as LangChain, LangGraph, and LlamaIndex. Deep Learning Frameworks
- Proficiency in PyTorch, TensorFlow, or Keras. Programming
- Strong programming skills in Python with experience in API development and microservices. Cloud & AI Infrastructure
- Experience deploying AI models on AWS, Azure, or Google Cloud Platform.
- Familiarity with MLOps pipelines, model serving, and AI lifecycle management. Vector Databases
- Hands-on experience with vector databases such as FAISS, Pinecone, ChromaDB, or Weaviate. Performance Optimization
- Experience optimizing LLM inference for speed, cost, and memory efficiency. Leadership & Collaboration

• Proven ability to lead AI projects and mentor engineering teams.
- Strong communication skills with the ability to translate business requirements into AI solutions.

Good to Have

- Experience with multimodal AI (text, image, video, speech).
- Familiarity with Docker, Kubernetes, and containerized AI deployment. • Experience with model serving frameworks such as FastAPI, Flask, or NVIDIA Triton.
- Exposure to distributed training and large-scale model training pipelines.

📌 Senior/Lead AI ML Engineer (Vadodara)
🏢 hiringhood
📍 Vadodara