AI/ML Developer

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:

• Design, develop, train, and deploy production-grade ML and GenAI models across use cases including NLP, computer vision, and structured data modeling.
• Leverage frameworks such as TensorFlow , Keras , PyTorch , and LangChain to build scalable deep learning and LLM-based solutions.
• Develop and maintain end-to-end ML pipelines with reusable, modular components for data ingestion, feature engineering, model training, and deployment.
• Implement and manage models on cloud platforms such as AWS , GCP , or Azure using services like SageMaker , Vertex AI , or Azure ML .
• Apply MLOps best practices using tools like MLflow , Kubeflow , Weights & Biases , Airflow , DVC , and Prefect to ensure scalable and reliable ML delivery.
• Incorporate CI/CD pipelines (using Jenkins, GitHub Actions, or similar) to automate testing, packaging, and deployment of ML workloads.
• Containerize applications using Docker and orchestrate scalable deployments via Kubernetes .
• Integrate LLMs with APIs and external systems using LangChain, Vector Databases (e.g., FAISS, Pinecone), and prompt engineering best practices.
• Collaborate closely with data engineers to access, prepare, and transform large-scale structured and unstructured datasets for ML pipelines.
• Build monitoring and retraining workflows to ensure models remain performant and robust in production.
• Evaluate and integrate third-party GenAI APIs or foundational models where appropriate to accelerate delivery.
• Maintain rigorous experiment tracking, hyperparameter tuning, and model versioning.
• Champion industry standards and evolving practices in ML lifecycle management , cloud-native AI architectures , and responsible AI.
• Work across global, multi-functional teams, including architects, principal engineers, and domain experts.

Essential Skills / Experience:

• 3+ 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.

Desirable Skills / Experience:

• Working knowledge of vector databases (e.g., FAISS , Pinecone , Weaviate ) and semantic search implementation.
• Hands-on experience with prompt engineering , fine-tuning LLMs, or using techniques like LoRA , PEFT , RLHF .
• Familiarity with data governance , privacy , and responsible AI guidelines (bias detection, explainability, etc.).
• Certifications in AWS, Azure, GCP, or ML/AI specializations.
• Experience in high-compliance industries like pharma , banking , or healthcare .
• Familiarity with agile methodologies and working in iterative, sprint-based teams.

Work Environment & Collaboration:

You will be a key member of an agile, forward-thinking AI/ML team that values curiosity, excellence, and impact. Our hybrid work culture promotes flexibility while encouraging regular in-person collaboration to foster innovation and team synergy. You'll have access to the latest technologies, mentorship, and continuous learning opportunities through hands-on projects and professional development resources.