Job Description

We seek a highly skilled ML Engineer with 3-5 years of industry experience specializing in AI/ML, MLOps, Azure cloud computing, and Python to collaborate with our team of Data Scientists, Data Engineers, and Subject Matter Experts (SMEs).  Candidate should have strong expertise in  traditional ML to design, develop, and deploy  industrial AI solutions for refinery and petrochemical operations. The role focuses on  time-series modeling, forecasting, optimization, and real-time deployment of ML systems that deliver measurable business impact (e.g., energy savings, yield improvement, process optimization). The candidate will develop, implement, and deploy advanced AI-driven solutions, including traditional ML, deep learning, neural networks, and advanced analytics tailored specifically to the refining and petrochemical industry.   What You'll Be DoingDesign, build and deploy  end-to-end AI/ML pipelines for industrial use cases at scale on cloud-based platforms or on-prem server Select and implement  appropriate AI/ML algorithms, and tools Build and maintain  data pipelines using  Python & SQL for reliable data flow between IT/OT systems Develop  time-series forecasting and  prediction models for process variables (e.g., temperature, pressure, yield, emissions, energy consumption) Build  optimization models for refinery & petrochemical operations (e.g., fuel optimization, throughput maximization, energy efficiency) Work with high-frequency process data from historians (e.g., PHD, OPC, SCADA systems) Perform  feature engineering on  multivariate time-series data, including lag features, rolling statistics, and domain-driven transformations  Train, evaluate, monitor, retrain and  maintain AI/ML models  Deploy models into  production using APIs, batch pipelines, or real-time streaming systems Implement  model monitoring, drift detection, and  retraining pipelines  Collaborate with  process engineers, SMEs, operations teams, and  business users to translate domain problems into ML solutions Ensure  scalability,  reliability, and  performance of  deployed AI systems  Regularly update and leverage  industry trends and  advancements in AI, ML, and  Optimization technologies. 
What We are looking for3-5 years of  experience in  AI/ML engineering roles Bachelors or Masters in Engineering, Computer Science, or equivalent experience   Strong programming skills in  Python, R, or a similar language  Robust understanding of  machine learning and  deep learning methodologies, including training, inferencing, and performance monitoring Strong experience in  time-series modeling, forecasting, and prediction problems Proven experience in  industrial/process data (Oil & Gas / Manufacturing preferred)  Solid expertise with  machine learning libraries (Numpy, Pandas, Matplotlib, Scikit-learn, Scipy, Seaborn, NLTK, Flask, Django) and frameworks (TensorFlow, PyTorch, Langchain, Langgraph).  Strong expertise in  SQL for data extraction, transformation, and pipeline building Understanding of  feature engineering for time-series data Experience in building  end-to-end ML systems (data → model → deployment → monitoring_>retraining) Hands-on experience with  model deployment Solid understanding of  MLOps concepts Model versioning CI/CD pipelines Monitoring & alerting Automated retraining Experience handling  large-scale data using PySpark / Apache Spark (good to have) Strong  analytical and  problem- solving capability in industrial contexts Ability to work closely with  cross-functional teams (operations + data + IT)  Clear written and  oral communication skills with a strong desire to share knowledge with business users, partners, and co-workers  
Good to Have (Not Mandatory)Exposure to Azure (Azure ML, Data Factory, Databricks, etc.) Experience with real-time data streaming (Kafka, MQTT, OPC integration) Exposure to GenAI / LLMs / Agentic systems (secondary skill, not core requirement) Knowledge of refinery / petrochemical processes (CDU, FCC, Cracker, Boilers, etc.)