Senior Machine Learning Engineer

the company is a global software development service company that helps businesses across the globe create next-generation software products. Founded in 2002, we unite 2,400+ tech-savvy professionals across 40+ countries, working on impactful projects for industry leaders and Fortune 500 companies. Our expertise spans cloud, data, AI/ML, embedded software, IoT, and more, driving digital transformation across finance, manufacturing, telecom, healthcare, and other industries. Join the company and become part of a team where your ideas make a real impact.

We are looking for a Senior Machine Learning Engineer to lead the development and deployment of advanced AI models. In this role, you will be responsible for the end-to-end lifecycle of our machine learning systems, from architectural design and data preprocessing to model training, optimization, and production deployment. 

You will work at the intersection of generative AI and traditional machine learning, building the engines that power two strategic AI initiatives spanning NLP-based document intelligence and predictive analytics. Operating within a structured agile delivery model with formal review gates, you will ensure our models are not just accurate, but also robust, explainable, and deployable within highly secure, on-premise environments. 

Key Responsibilities:

1. LLM and NLP Pipelines 

• Design and fine-tune Large Language Model (LLM) pipelines to interpret complex regulatory texts (e.g. standards, building codes) and extract structured rules. 

• Convert natural language requirements into computer-processable formats (e.g., logic tuples) that can be executed by downstream compliance engines. 

• Implement RAG (Retrieval-Augmented Generation) architectures to enable semantic querying of technical documentation and historical project data. 

• Optimize prompt strategies (few-shot learning, chain-of-thought) to improve model performance on domain-specific tasks without extensive retraining.

1. Predictive and Analytical Models (Supply Chain) 

• Develop time-series forecasting models to predict material demand and spend categories, integrating internal ERP data with external market signals. 

• Build classification and anomaly detection models to assess supplier risk profiles based on financial health, delivery performance, and geopolitical factors. 

• Design algorithms for multi-objective optimization(e.g., balancing cost vs. lead time vs. risk) to support procurement decision-making. 

1. MLOps and Productionization 

• Containerize models using Docker/Kubernetes and deploy them into secure, on-premise inference environments. 

• Build automated training and inference pipelines using tools like Kubeflow or MLflow to ensure reproducibility and scalability. 

• Optimize model inference latency and resource usage (e.g., quantization, distillation) to run efficiently on available hardware. 

• Implement monitoring systems to track model drift and performance in production, establishing feedback loops for continuous improvement. 

Requirements:

•  5+ years of experience in Machine Learning Engineering, with a proven track record of deploying models into production environments. 

• Expert proficiency in Python and standard ML libraries (PyTorch, TensorFlow, Scikit-learn, Pandas, NumPy). 

• Strong experience with transformer architectures (BERT, GPT, Llama) and NLP frameworks (Hugging Face, LangChain). 

• Proficiency with MLOps tools and practices, including containerization (Docker), orchestration (Kubernetes), and experiment tracking (MLflow). 

• Ability to design data preprocessing pipelines for both structured (SQL, tabular) and unstructured (text, PDF) data. 

• Strong grasp of algorithmic principles for implementing custom logic, such as graph traversal or geometric computations.

• Ability to quickly learn and apply ML techniques to specialized domains like engineering, supply chain, or other highly regulated industries. 

• Experience working in agile environments (Sprints) while adhering to rigorous engineering standards and documentation requirements.

• Strong communication skills to work effectively with Data Scientists, Backend Engineers, and Domain Experts to align technical solutions with business needs.