Must have :

- Minimum of 2 years of experience in data science building and deploying ML models in production environments

- Strong programming skills in Python with experience in ML frameworks (scikit-learn, TensorFlow, PyTorch, XGBoost)

- Experience building data pipelines and ML infrastructure (Airflow, Kubernetes, Docker, cloud platforms)

- Proficiency in SQL and database systems

- Experience with MLOps practices including versioning, monitoring, and CI/CD for ML systems

- Ability to work independently on ambiguous problems with minimal guidance

- Strong communication skills for client-facing technical discussions

- Excellent skills in SQL, Python & R

- Predictive modeling and statistical learning

- Strong understanding of cloud-based platforms like Azure, AWS, GCP is a mandatory requirement for model development, testing and deployment in optimized environments

Good to have :

- Hands-on experience with GenAI technologies including LLMs, prompt engineering, vector databases, and orchestration frameworks (LangChain, LlamaIndex) - Familiarity with NLP, computer vision, and Gen AI applications

- Deep learning and neural network architectures

- GenAI orchestration and LLM integration

Roles & Responsibilities :

- Design and develop predictive models and machine learning pipelines for client deployment

- Build and orchestrate GenAI solutions including prompt engineering, RAG systems, and LLM integration

- Engineer production ML systems including data pipelines, model serving infrastructure, and monitoring frameworks

- Translate client business problems into technical ML solutions with minimal supervision

- Deploy models to production environments and ensure system reliability and performance

- Collaborate with engineering teams to integrate ML systems into client applications

- Build ML infrastructure and tooling from foundational components where existing systems are insufficient - Communicate technical approaches and results to both technical and non-technical stakeholders

- Maintain model performance through monitoring, retraining, and iterative improveme