3+ PyTorch Jobs in Ahmedabad | PyTorch Job openings in Ahmedabad

Exp: 4 to 8 Years

CTC: up to 40 LPA

Mandatory Criteria

• 5–7 years of hands-on experience in building and deploying AI solutions, ideally in fintech or financial services.
• Strong coding skills in Python and familiarity with libraries like TensorFlow, PyTorch, scikit-learn, XGBoost, etc.
• Experience with NLP, time series forecasting, anomaly detection, or graph ML relevant to fintech applications.
• Solid understanding of MLOps concepts – model versioning, deployment, monitoring.
• Experience working with cloud platforms (AWS/GCP/Azure) and ML services (e.g., SageMaker, Vertex AI).
• Familiarity with Docker, Kubernetes, or other containerization tools for model deployment is a plus.
• Comfortable working with large-scale structured and unstructured datasets. Experience with LLMs or generative AI for fintech-specific use cases.
• Exposure to RegTech, risk modeling, or algorithmic trading.
• Publications, GitHub contributions, or Kaggle competitions.
• Familiarity with SQL/NoSQL databases and data pipelines (e.g., Airflow, Spark, etc.).

If interested kindly share your updated resume on 82008 31681

Job Description: Machine Learning Engineer – LLM, Agentic AI, Computer Vision, and MLOps

Location: Ahmedabad

Experience: 4 to 6 years

Employment Type: Full-Time

About Us

Join a forward-thinking team at Tecblic, where innovation meets cutting-edge technology. We specialize in delivering AI-driven solutions that empower businesses to thrive in the digital age. If you're passionate about LLMs, Computer Vision, MLOps, and pushing the boundaries of Agentic AI, we’d love to have you on board.

Key Responsibilities

• Research and Development: Design, develop, and fine-tune machine learning models across LLM, computer vision, and Agentic AI use cases.
• Model Optimization: Fine-tune and optimize pre-trained models, ensuring performance, scalability, and minimal latency.
• Computer Vision: Build and deploy vision models for object detection, classification, OCR, and segmentation.
• Integration: Work closely with software and product teams to integrate models into production-ready applications.
• Data Engineering: Develop robust data pipelines for structured, unstructured (text/image/video), and streaming data.
• Production Deployment: Deploy, monitor, and manage ML models in production using DevOps and MLOps practices.
• Experimentation: Prototype and test new AI approaches such as reinforcement learning, few-shot learning, and generative AI.
• DevOps Collaboration: Collaborate with the DevOps team to ensure CI/CD pipelines, infrastructure-as-code, and scalable deployments are in place.
• Technical Mentorship: Support and mentor junior ML and data engineers.

Requirements

Core Technical Skills

• Strong Python skills for machine learning and computer vision.
• Hands-on experience with PyTorch, TensorFlow, Hugging Face, Scikit-learn, OpenCV.
• Deep understanding of LLMs (e.g., GPT, BERT, T5) and Computer Vision architectures (e.g., CNNs, Vision Transformers, YOLO, R-CNN).
• Strong knowledge of NLP tasks, image/video processing, and real-time inference.
• Experience in cloud platforms: AWS, GCP, or Azure.
• Familiarity with Docker, Kubernetes, and serverless deployments.
• Proficiency in SQL, Pandas, NumPy, and data wrangling techniques.

DevOps & MLOps Skills

• Experience with CI/CD tools such as GitHub Actions, GitLab CI, Jenkins, etc.
• Knowledge of Infrastructure as Code (IaC) tools like Terraform, CloudFormation, or Pulumi.
• Familiarity with container orchestration and Kubernetes-based ML model deployment.
• Hands-on experience with ML pipelines and monitoring tools: MLflow, Kubeflow, TFX, or Seldon.
• Understanding of model versioning, model registry, and automated testing/validation in ML workflows.
• Exposure to observability and logging frameworks (e.g., Prometheus, Grafana, ELK stack).

Additional Skills (Good to Have)

• Knowledge of Agentic AI systems and use cases.
• Experience with generative models (e.g., GANs, VAEs) and RL-based architectures.
• Prompt engineering and fine-tuning for LLMs in specialized domains.
• Working with vector databases (e.g., Pinecone, FAISS, Weaviate).
• Distributed data processing using Apache Spark, Dask.

General Skills

• Strong foundation in mathematics, including linear algebra, probability, and statistics.
• Deep understanding of data structures and algorithms.
• Comfortable handling large-scale datasets, including images, video, and multi-modal data.

Soft Skills

• Strong analytical and problem-solving mindset.
• Excellent communication skills for cross-functional collaboration.
• Self-motivated, adaptive, and committed to continuous learning.