Senior AI/ML Engineer

ROLE : Data Scientist

LOCATION : Ahmedabad (Only Ahmedabad Candidates Can apply)

EXPERIENCE: 2.5 to 4+ yeas

JOB SUMMARY: We are seeking a talented and driven Data Scientist with 3+ years of hands-on industry experience to join our AI & Data Science team. The ideal candidate will combine deep expertise in machine learning, deep learning, and Generative AI with strong software engineering practices including DevOps, containerization, and CI/CD pipelines. This role demands a practitioner who can independently design, train, fine-tune, and deploy ML models at scale, architect GenAI-powered solutions, and communicate insights effectively to both technical and non-technical stakeholders.

KEY RESPONSIBILITIES: Machine Learning & Deep Learning · Design, develop, train, and fine-tune ML/DL models for production-grade applications. · Apply transfer learning and model fine-tuning techniques on large-scale datasets. · Evaluate and benchmark model performance; implement improvements iteratively. · Develop and maintain end-to-end ML pipelines from data ingestion to model serving. Generative AI & LLM Engineering · Design and implement GenAI architectures including RAG, agents, and multi-modal pipelines. · Build and orchestrate LLM workflows using LangChain and LangGraph frameworks. · Conduct prompt engineering, context optimization, and output evaluation for LLMs. · Integrate GenAI solutions into scalable APIs and enterprise-grade applications. Computer Vision · Develop and deploy computer vision models for object detection, classification, segmentation, and tracking. · Work with CNN-based architectures (ResNet, EfficientNet, YOLO) and Vision Transformers (ViT, DETR). · Implement real-time inference pipelines optimized for edge and cloud deployment. DevOps, Containerization & CI/CD · Containerize ML applications using Docker; orchestrate deployments with Kubernetes, n8n. · Build and maintain CI/CD pipelines (GitHub Actions, Jenkins, or GitLab CI) for automated model training, testing, and deployment. · Implement MLOps best practices including model versioning, experiment tracking, and monitoring. · Collaborate with infrastructure teams on cloud platforms (AWS / GCP / Azure). Client & Stakeholder Engagement · Engage directly with clients to gather requirements, understand business challenges, and translate them into technical specifications. · Present findings, model results, and AI recommendations to non-technical audiences clearly and confidently. · Prepare detailed documentation, reports, and proposals for internal and external stakeholders.

REQUIRED SKILLS Core Technical Skills · Proficiency in Python and ML ecosystem libraries: NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch. · Hands-on experience with deep learning model training, fine-tuning, and transfer learning. · Strong understanding of supervised, unsupervised, and reinforcement learning paradigms. · Solid knowledge of data preprocessing, feature engineering, and statistical analysis. Generative AI & LLM Skills · Practical experience with LLMs (GPT-4, LLaMA, Mistral, Gemini, Claude, etc.). · Hands-on proficiency with LangChain and LangGraph for agentic and multi-step AI workflows. · Knowledge of RAG (Retrieval-Augmented Generation), vector databases (Pinecone, Weaviate, ChromaDB, FAISS). · Experience with prompt engineering, few-shot learning, and evaluation frameworks. Computer Vision Skills · Experience with OpenCV, torchvision, and Hugging Face for vision tasks. · Knowledge of YOLO, Segment Anything Model (SAM), or similar frameworks. · Experience with image preprocessing, augmentation, and large-scale image dataset management. DevOps & Engineering Skills · Proficient with Docker (multi-stage builds, image optimization) and Kubernetes (deployments, services, HPA). · Hands-on CI/CD pipeline design using GitHub Actions, Jenkins, or GitLab CI. · Experience with ML experiment tracking tools: MLflow, Weights & Biases, or DVC. · Familiarity with REST API development using FastAPI or Flask for model serving. · Working knowledge of Git version control and collaborative development workflows.

EDUCATION · Bachelor's degree in Computer Science, Data Science, Mathematics, Statistics, or a related quantitative field (required). · Master's degree in Artificial Intelligence, Machine Learning, or a related field (preferred). · Relevant certifications in ML/AI (e.g., TensorFlow Developer, AWS ML Specialty, Google Professional ML Engineer) are a plus.