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

• · Bachelors degree in Computer Science, Data Science, Mathematics, Statistics, or a related quantitative field (required).
• · Masters 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.