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.