SKILL
:Python, Tensorflow,Gen AI (Must Have), Machine learning ,AWS, Agentic AI, Claude, Fast API (Good to have)
EXP
: 3 -10 yrs
Introduction to the Role:
Are you passionate about building intelligent systems that learn, adapt, and deliver real-world value Join our high-impact AI & Machine Learning Engineering team and be a key contributor in shaping the next generation of intelligent applications. As an AI/ML Engineer , youll have the unique opportunity to develop, deploy, and scale advanced ML and Generative AI (GenAI) solutions in production environments, leveraging cutting-edge technologies, frameworks, and cloud platforms .
In this role, you will collaborate with cross-functional teams including data engineers, product managers, MLOps engineers, and architects to design and implement production-grade AI solutions across domains. If you're looking to work at the intersection of deep learning, GenAI, cloud computing, and MLOps this is the role for you.
Accountabilities:
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Design, develop, train, and deploy production-grade ML and GenAI models across use cases including NLP, computer vision, and structured data modeling.
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Leverage frameworks such as TensorFlow , Keras , PyTorch , and LangChain to build scalable deep learning and LLM-based solutions.
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Develop and maintain end-to-end ML pipelines with reusable, modular components for data ingestion, feature engineering, model training, and deployment.
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Implement and manage models on cloud platforms such as AWS , GCP , or Azure using services like SageMaker , Vertex AI , or Azure ML .
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Apply MLOps best practices using tools like MLflow , Kubeflow , Weights & Biases , Airflow , DVC , and Prefect to ensure scalable and reliable ML delivery.
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Incorporate CI/CD pipelines (using Jenkins, GitHub Actions, or similar) to automate testing, packaging, and deployment of ML workloads.
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Containerize applications using Docker and orchestrate scalable deployments via Kubernetes .
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Integrate LLMs with APIs and external systems using LangChain, Vector Databases (e.g., FAISS, Pinecone), and prompt engineering best practices.
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Collaborate closely with data engineers to access, prepare, and transform large-scale structured and unstructured datasets for ML pipelines.
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Build monitoring and retraining workflows to ensure models remain performant and robust in production.
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Evaluate and integrate third-party GenAI APIs or foundational models where appropriate to accelerate delivery.
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Maintain rigorous experiment tracking, hyper parameter tuning, and model versioning.
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Champion industry standards and evolving practices in ML lifecycle management , cloud-native AI architectures , and responsible AI.
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Work across global, multi-functional teams, including architects, principal engineers, and domain experts.
Essential Skills / Experience:
- 3-10 years of hands-on experience in developing, training, and deploying ML/DL/GenAI models .
- Strong programming expertise in Python with proficiency in machine learning , data manipulation , and scripting .
- Demonstrated experience working with Generative AI models and Large Language Models (LLMs) such as GPT, LLaMA, Claude, or similar.
- Hands-on experience with deep learning frameworks like TensorFlow , Keras , or PyTorch .
- Experience in LangChain or similar frameworks for LLM-based app orchestration.
- Proven ability to implement and scale CI/CD pipelines for ML workflows using tools like Jenkins , GitHub , GitLab , or Bitbucket Pipelines .
- Familiarity with containerization (Docker) and orchestration tools like Kubernetes .
- Experience working with cloud platforms (AWS, Azure, GCP) and relevant AI/ML services such as SageMaker , Vertex AI , or Azure ML Studio .
- Knowledge of MLOps tools such as MLflow , Kubeflow , DVC , Weights & Biases , Airflow , and Prefect .
- Strong understanding of data engineering concepts , including batch/streaming pipelines, data lakes, and real-time processing (e.g., Kafka ).
- Solid grasp of statistical modeling , machine learning algorithms , and evaluation metrics.
- Experience with version control systems (Git) and collaborative development workflows.
- Ability to translate complex business needs into scalable ML architectures and systems.
