About this role
<div><div style="padding:10.0px 0.0px;border:1.0px solid transparent"><div style="word-wrap:break-word"><H2 style="margin:0.0px">Job Purpose</H2> </div><div><p style="text-align:left;margin:0.0cm"><span style="">TechnipFMC leads the transformation of the energy industry by transforming our clients’ project economics through fully integrated projects, products, and services. Making robust decisions efficiently and consistently by using data about our products, processes, and operations is a key competency for our business to achieve our true north. </span></p> <p style="text-align:left;margin:0.0cm"> </p> <p style="text-align:left;margin:0.0cm"><span style="">In this context, the business is developing its Advanced Analytics capability with the aim of better leveraging our data to deliver new insights, value and smarter ways of working across our </span><span style="">value stream. <strong>Machine Learning Engineering</strong> is a key discipline in this context that focuses on designing, building, and deploying scalable machine learning systems and infrastructure to enable data-driven decision-making and innovation.</span></p> <p style="text-align:left;margin:0.0cm"> </p> <p style="text-align:left;margin:0.0cm"><span style="">This role is for a <strong>Machine Learning Engineer </strong>who will be a member of the Advanced Analytics team (within Software Services) that is responsible for developing the company’s data analytics strategy and roadmap.</span></p></div></div><div style="padding:10.0px 0.0px;border:1.0px solid transparent"><div style="word-wrap:break-word"><H2 style="margin:0.0px">Job Description</H2> </div><div><p style="text-align:left;background-color:#fafafa;margin:0.0cm"><strong><span style="">Health, Safety & Environment:</span></strong></p> <ul> <li style=""><span style="">Complete mandatory HSE courses and implement any recommended safety actions efficiently. </span></li> <li style=""><span style="">Be a consistent role model in relation to safety practices with a commitment to the importance of safety</span></li> </ul> <p style="text-align:left;background-color:#fafafa;margin:0.0cm"><strong><span style="">Performance & Delivery:</span></strong></p> <ul> <li style=""><span style="">Optimize model performance through hyperparameter tuning, feature engineering, and algorithm selection.</span></li> <li style=""><span style="">Collaborate with data scientists to translate prototypes into production-ready solutions.</span></li> <li style=""><span style="">Design and implement scalable machine learning pipelines for training, validation, and deployment.</span></li> <li style=""><span style="">Develop APIs and services to integrate machine learning models into enterprise applications.</span></li> <li style=""><span style="">Ensure robustness and reliability of ML systems through unit testing, integration testing, and CI/CD practices.</span></li> <li style=""><span style="">Monitor model performance in production and implement retraining strategies as needed.</span></li> <li style=""><span style="">Leverage cloud platforms (e.g., AWS, Azure, GCP) and containerization tools (e.g., Docker, Kubernetes) for scalable deployment.</span></li> <li style=""><span style="">Apply best practices in software engineering, including version control, code reviews, and documentation.</span></li> <li style=""><span style="">Manage infrastructure for data ingestion, model training, and inference at scale.</span></li> <li style=""><span style="">Implement model governance practices, including auditability, reproducibility, and compliance.</span></li> <li style=""><span style="">Collaborate with cross-functional teams including DevOps, software engineers, and product managers.</span></li> <li style=""><span style="">Stay current with advancements in ML engineering tools, frameworks, and deployment strategies.</span></li> <li style=""><span style="">Utilize a broad range of technologies including deep learning frameworks (e.g., TensorFlow, PyTorch), MLOps tools (e.g., MLflow, Kubeflow), and distributed computing (e.g., Spark, Ray).</span></li> <li style=""><span style="">Communicate results effectively to both technical and non-technical stakeholders.<br></span></li> </ul></div></div><div style="padding:10.0px 0.0px;border:1.0px solid transparent"><div style="word-wrap:break-word"><H2 style="margin:0.0px">You are meant for this job if:</H2> </div><div><p><strong><span style="">REQUIRED KNOWLEDGE/FORMAL EDUCATION:</span></strong></p> <ul> <li style=""><span style="">Required: Bachelor’s degree in Computer Science, Statistics or Mathematics.</span></li> <li style=""><span style="">Desirable: Master’s or a higher degree in Computer Science, Statistics or Mathematics. or a related discipline</span></li> </ul> <p style="text-align:left;margin:0.0cm"><strong><span style=""> REQUIRED EXPERIENCE:</span></strong></p> <ul> <li style=""><span style="">Minimum of 5 years of experience in machine learning engineering, building and deploying advanced solutions using state-of-the-art ML techniques.</span></li> <li style=""><span style="">Designing and implementing machine learning systems to solve problems in the oil and gas industry.</span></li> <li style=""><span style="">Collaborating with business and technical stakeholders to deliver scalable and tailored ML solutions.</span></li> <li style=""><span style="">Manage delivery of machine learning project milestones, ensuring on-time & on-quality deployment.</span></li> <li style=""><span style="">Ability to evaluate and guide technical work performed by junior machine learning engineer.</span></li> </ul> <p> </p></div></div><div style="padding:10.0px 0.0px;border:1.0px solid transparent"><div style="word-wrap:break-word"><H2 style="margin:0.0px">Long Description</H2> </div><div><p><strong><span style="">REQUIRED TECHNICAL SKILLS AND/OR PROBLEM-SOLVING SKILLS:</span></strong></p> <ul> <li style=""><span style="">Advanced – Programming in Python (preferred), Java, SQL, and Scala.</span></li> <li style=""><span style="">Advanced – Use of ML libraries and tools such as scikit-learn, NumPy, Pandas, and joblib.</span></li> <li style=""><span style="">Advanced – Designing, training, and deploying ML models for diverse data types including tabular, unstructured (e.g., text, images), and time-series data.</span></li> <li style=""><span style="">Advanced – Working with high-performance ML frameworks such as TensorFlow, PyTorch, and ONNX.</span></li> <li style=""><span style="">Advanced – Using version control systems like Git for collaborative development and code management.</span></li> <li style=""><span style="">Advanced – Managing the ML lifecycle using tools like MLflow, Docker, Kubernetes, and Airflow.</span></li> <li style=""><span style="">Advanced – Building and exposing ML models via APIs using tools like FastAPI, Flask, TensorFlow Serving, or TorchServe</span></li> <li style=""><span style="">Proficient – Implementing MLOps practices for production-grade ML pipelines on cloud platforms (e.g., AWS SageMaker, Azure ML, or GCP Vertex AI).</span></li> <li style=""><span style="">Proficient – Monitoring and observability of ML systems using tools like Prometheus, Grafana, and Seldon Core.</span></li> <li style=""><span style="">Proficient – Working with SQL and NoSQL databases including MySQL, PostgreSQL, MongoDB, and Cassandra.</span></li> <li style=""><span style="">Proficient – Familiarity with generative AI, foundation models, and LLMs to stay aligned with emerging trends in ML engineering.</span></li> </ul></div></div></div>