About the Role

We are looking for an AI / Machine Learning Engineer to work on cutting-edge data science, machine learning, and Generative AI problems across multiple industries. The role involves working closely with clients and stakeholders to understand business challenges and develop scalable AI/ML solutions for complex, unstructured problems.

Key Responsibilities

- Architect, develop, and deploy machine learning models and AI solutions into production environments.
- Partner with data scientists to convert ML prototypes into scalable, enterprise-ready applications.
- Design and implement enterprise AI solutions using LLMs, RAG, Knowledge Graphs, and Multi-Agent Systems.
- Integrate AI/ML models into existing products, APIs, and enterprise applications.
- Perform experimentation, model fine-tuning, validation, and error analysis to improve model accuracy and reliability.
- Monitor, maintain, and retrain deployed models to ensure optimal performance.
- Work with stakeholders to understand business requirements and translate them into effective AI-driven solutions.
- Stay updated with the latest developments in AI/ML, Generative AI, MLOps, and cloud-native AI technologies.
- Follow responsible AI and ethical AI practices throughout the AI development lifecycle.
- Collaborate with cross-functional teams including data scientists, engineers,



product teams, and business stakeholders.

Mandatory Skills

- Strong programming skills in Python.
- Hands-on experience with machine learning frameworks such as TensorFlow, PyTorch, and scikit-learn.
- Robust understanding of Machine Learning, Deep Learning, and Generative AI concepts.
- Experience working with LLMs and Foundation Models such as OpenAI, Hugging Face, Anthropic, etc.
- Experience with Prompt Engineering and LLM application development.
- Hands-on experience with GenAI frameworks/tools such as:
- LangGraph
- AutoGen
- OpenAI SDK

- Understanding of Retrieval-Augmented Generation (RAG) and LLM-based applications.
- Strong analytical, problem-solving, and data interpretation skills.
- Ability to work in a fast-paced, deadline-driven environment.
- Strong ownership, learning ability, and passion for technology.

Good to Have / Nice-to-Have Skills

- Experience with MLOps tools and practices:
- MLflow
- Kubeflow
- Airflow
- Docker
- Kubernetes

- Experience with Big Data technologies such as:

- Apache Spark
- Hadoop
- Databricks

- Exposure to Responsible AI, AI governance, bias detection, and model fairness.
- Contributions to open-source AI/ML projects.
- Experience deploying AI/ML solutions on cloud platforms.

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