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Let’s do this. Let’s change the world. We are looking for a highly motivated expert Machine Learning Engineer to design and develop scalable, secure, and reliable data pipelines and ingestion solutions that power knowledge layers and assistant experiences via generative AI solutions for our Manufacturing Applications Product Team. The Senior Machine Learning Engineer will design, build, and scale AI-powered solutions that transform manufacturing operations. This role will focus on developing production-grade GenAI and machine learning systems, including retrieval-augmented generation (RAG), agentic workflows, knowledge layers, and intelligent assistants. The ideal candidate combines deep expertise in data engineering, distributed computing, MLOps, and modern AI platforms to deliver secure, scalable, and reliable solutions that enable advanced analytics, automation, and decision support across manufacturing operations.
Job Responsibility
Design, deploy, monitor, and optimize production-grade ML and Generative AI applications for AI-enabled manufacturing solutions
Define technical architecture, engineering standards, and best practices across data engineering, ML, GenAI, analytics, and platform capabilities
Partner with business stakeholders, product owners, and cross-functional teams to translate manufacturing challenges into secure, scalable, production-ready AI and data solutions
Design, develop, and maintain complex ETL/ELT pipelines in Databricks using PySpark, Scala, and SQL for large-scale structured and unstructured data processing
Build efficient ingestion, transformation, migration, and deployment pipelines across databases, APIs, logs, event streams, images, PDFs, documents, and third-party platforms
Design and implement GenAI solutions including RAG, embeddings, vector databases, agentic workflows, tool-calling systems, LLM orchestration, serving optimization, knowledge graphs, and metadata-driven retrieval
Build GenAI applications using frameworks and platforms such as LangChain, LangGraph, LlamaIndex, DSPy, OpenAI APIs, Amazon Bedrock, or equivalent technologies
Develop evaluation and observability frameworks to monitor model quality, hallucination rates, drift, retrieval effectiveness, latency, token usage, cost, reliability, operational health, and business impact
Build and maintain MLOps and LLMOps capabilities, including experiment tracking, model registry, prompt management, versioning, CI/CD, automated testing, deployment automation, monitoring, governance, and release controls
Design scalable data quality, validation, security, privacy, access control, logging, governance, and interoperability capabilities across hybrid cloud environments
Automate manual processes, develop reusable frameworks and accelerators, and continuously improve engineering productivity, system reliability, and delivery efficiency
Work in Agile/SAFe environments using JIRA, Confluence, and Agile DevOps tools to manage delivery, documentation, backlogs, user stories, and engineering execution
Requirements
Hands-on experience with AWS, Databricks, Apache Spark, PySpark, SparkSQL, Python, and SQL for large-scale data engineering
Strong proficiency in workflow orchestration, Spark performance tuning, and scalable batch and streaming data pipeline development
Experience with real-time data processing and integration using Apache Kafka, Debezium, or similar streaming technologies
Hands-on experience with MLOps tools and practices, including MLflow, model serving, feature stores, experiment tracking, deployment, and lifecycle management
Experience with GenAI engineering practices, including prompt engineering, LLM evaluation, AI observability, agentic workflows, and knowledge graphs
Ability to design and develop APIs or service interfaces for data, ML, and GenAI application integration
Experience with Agile/SAFe delivery models, DevOps practices, CI/CD concepts, and cross-functional team collaboration
Strong analytical, problem-solving, debugging, communication, and teamwork skills
Ability to quickly learn, adapt, and apply emerging technologies across data, ML, and AI engineering
Doctorate degree / Master's degree / Bachelor's degree and 8 to 13 years of experience years of experience in Computer Science, IT or related field
Nice to have
Experience with data engineering, analytics, ML, or AI solutions in pharma, biotech, manufacturing, or other regulated industries
Familiarity with manufacturing systems and industrial data sources such as SCADA, Data Historian, MES, ERP, LIMS, or related platforms
Experience with SQL, NoSQL, vector databases, knowledge graphs, data modeling, and OLAP/OLTP performance tuning
Experience with Scala for Spark-based data engineering and distributed data processing
Experience with Kubernetes or container orchestration platforms for scalable deployment, model serving, and production operations
Experience applying software engineering best practices, including Git, automated testing, CI/CD, code reviews, and DevOps practices
Experience using AI-assisted development tools such as GitHub Copilot, Cursor, Claude Code, or similar tools
Experience collaborating with ML engineers, prompt engineers, product managers, product owners, architects, and business stakeholders on AI and data initiatives
Experience designing APIs or service interfaces to expose data, ML, or GenAI capabilities to consumers