Saras Analytics - Artificial Intelligence/Machine Learning Engineer - RAG/LLM

We are seeking a highly skilled and motivated AI/ML Engineer to join our team.

The ideal candidate will have extensive experience in developing, deploying, and optimizing machine learning models and pipelines.

You will be responsible for automating workflows, transitioning proofs of concept (POCs) into production environments, and building scalable solutions such as customer segmentation, demand forecasting, and market mix models.

Additionally, you will work with cutting-edge AI advancements, including Retrieval-Augmented Generation (RAG) and AI Agents, contributing to end-to-end automated Data Science solutions.

Key Responsibilities:

- Design, develop, and implement machine learning pipelines for automated workflows and customer insights generation.

- Build and optimize predictive models for applications such as demand forecasting, inventory forecasting, and market mix modelling.

- Develop natural language processing (NLP) modules leveraging techniques such as Retrieval-Augmented Generation (RAG) and AI Agents with LLMs.

- Deploy machine learning models into production environments, ensuring scalability, reliability, and efficiency.

- Transition POCs from experimentation to fully operational, production-grade systems.

- Collaborate with cross-functional teams, including data scientists, engineers, and stakeholders, to translate business needs into ML solutions.

- Apply containerization tools (e.g., Docker) and orchestration platforms (e.g., Kubernetes) for efficient deployment and lifecycle management of ML models.

- Monitor, evaluate, and refine machine learning models to maintain performance and accuracy.

- Stay updated on advancements in AI, machine learning, and data science technologies.

Required Qualifications:

- Bachelors or Masters degree in Computer Science, Machine Learning, Data Science, or a related field.

- Proven experience in machine learning engineering or a similar role.

- Proficiency in Python and its data science libraries.

- Hands-on experience with ML frameworks such as PyTorch and TensorFlow.

- Strong knowledge of data pre-processing, feature engineering, and model evaluation methodologies.

- Understanding of NLP concepts, including pre-processing, embeddings, entity recognition, and vector

storage.

- Experience with containerization (Docker) and orchestration tools (Kubernetes).

- Demonstrated ability to transition POCs into scalable production solutions.

- Experience developing models for demand forecasting, inventory forecasting, or marketing analytics.

- Familiarity with cloud platforms such as GCP and Azure, and their machine learning services.

- Understanding of LLM architectures, statistical modelling, and advanced analytics techniques.

- Proven ability to build, debug, and optimize large, high-performance ML training jobs.

- Excellent problem-solving skills and a collaborative mindset.

Preferred Skills:

- Experience with CI/CD pipelines for ML model integration and deployment.

- Familiarity with MLOps tools and practices.

- Strong communication skills to explain technical concepts to non-technical stakeholders.

What We Offer:

- Competitive salary and comprehensive benefits package.

- Opportunities to work on innovative and impactful AI projects.

- A collaborative and inclusive work environment.

- Continuous learning and professional development opportunities.