Applied AI ML Associate Senior

• Design and deliver agent-based artificial intelligence solutions that address defined business use cases from prototype through production deployment.
• Implement human-in-the-loop workflows, fallback behaviors, and tool-calling patterns to improve reliability, traceability, and user outcomes.
• Evaluate candidate language models for specific use cases by defining metrics, running experiments, and documenting performance, limitations, and trade-offs.
• Build reusable artificial intelligence tools and integrations that connect models and agents to enterprise data and services.
• Develop end-to-end machine learning pipelines that prepare data, train or adapt models, validate quality, and support repeatable deployments.
• Apply retrieval-augmented generation methods to improve answer accuracy and grounding on large datasets.
• Use deep learning techniques (including attention-based architectures) to support solutions such as semantic search, entity resolution, forecasting, and anomaly detection where appropriate.
• Own small-to-medium engineering deliverables end-to-end, including requirements clarification, design, implementation, testing, release, and post-release support.
• Collaborate with cross-functional partners to establish baselines, analyze results, and translate findings into actionable improvements.
• Incorporate governance, risk, and control considerations into solution design and delivery to support safe and responsible use of machine learning capabilities.

Required qualifications, capabilities, and skills 

• Master’s degree or higher in Computer Science, Data Science, or a related discipline (or equivalent).
• 5+ years of industry experience, including a minimum of 3 years hands-on experience as a machine learning engineer, or data scientist.
• Demonstrated experience applying machine learning methods such as regression, classification, clustering, time series modeling, causal inference, or mathematical optimization.
• Hands-on experience designing and building agent-based solutions and tool integrations using agent frameworks (for example, Google ADK, LangGraph) and agent-to-agent communication patterns.
• Proficiency in Python for production-oriented development (testing, packaging, and deployment practices).
• Hands-on experience building data pipelines and working with large datasets; experience with PySpark is acceptable where relevant.
• Working knowledge of deep learning frameworks (TensorFlow or PyTorch) and modern natural language processing techniques.
  • Strong written and verbal communication skills with experience partnering with stakeholders across engineering and business functions.