Role & responsibilities Position Overview The Engineer, Artificial Intelligence supports the development and deployment of advanced artificial intelligence solutions. This role focuses on implementing solutions that leverage Machine Learning, Natural Language Processing, and emerging technologies such as Generative AI and Large Language Models. The Engineer, Artificial Intelligence will work closely with data scientists, ML engineers, and product teams to translate business needs into scalable AI systems. Essential Duties Include, but are not limited to, the following: Support the execution of ML, NLP, LLM deliverables aligned with AI strategies. Contribute to the development of custom ML, Gen AI, NLP, LLM models for batch and stream processing-based AI ML pipelines including data ingestion, preprocessing modules, search and retrieval, Retrieval Augmented Generation (RAG), and NLP/LLM model development. Collaborate closely with data scientists, machine learning engineers, and software engineers to integrate AI models into production systems. Stay current with emerging AI/ML technologies and recommend innovative approaches. Apply best practices in data privacy, security, and biases in AI systems. Minimum Qualifications: Bachelors degree in Computer Science, Artificial Intelligence, Data Science, or a related field; or High School Diploma/General Education Degree and 4+ years of relevant as outlined in the essential duties in lieu of Bachelor’s Degree. Professional experience in AI/ML model development. Demonstrated ability working with machine learning frameworks, programming languages like Python, and cloud platforms. Demonstrated ability to learn new technologies. Demonstrated understanding of ethical considerations in AI systems. Strong analytical and problem-solving skills with understanding of AI/ML techniques. Preferred Qualifications: Experience deploying Gen AI solutions at scale. Experience fine tuning LLMs, SLMs, teacher-student frameworks and model distillation. Familiarity with human in the loop methods for aligning LLMs with human preferences. Familiarity with agentic framework platforms and concepts. Experience deploying agentic-based solutions is a plus.
Role & responsibilities Essential Duties Include but are not limited to the following: Apply machine learning, deep learning, and artificial intelligence techniques. Use advanced analytics methods to extract value from business data. Perform large-scale experimentation and build data-driven models to answer business questions. Create hypotheses and experiments to identify hidden relationships and construct new analytics methods. Develop and implement Markov decision process models and healthcare economic models. Visualize information and develop reports on results of data analysis. Influence product teams through presentation of data-based recommendations. Spread best practices to analytics and product teams. Implement new tools to make data analysis more efficient. Ability to communicate complex results to technical and non-technical audiences. Excellent verbal and written communication skills. Support and comply with the companys Quality Management System policies and procedures. Minimum Qualifications Bachelors degree in computer science, data science, information technology, or related field. 3+ years of professional experience working with large datasets for drawing business insights. 1+ years of experience in a data science role. Professional working knowledge of machine learning; including solving complex business problems, predictive modeling, leveraging both structured and unstructured data sources, relational databases, and SQL. Professional working knowledge of statistics and modeling techniques. Fluent in Python or R programming. Demonstrated ability to learn new technologies. Proficient in Microsoft Office. Preferred Qualifications Master’s degree in science, technology, engineering, mathematics, or related field. Experience in cloud environments. Professional working knowledge of distributed computing and big data technologies, such as Spark or related technologies. Professional working knowledge of statistical methodologies and tools (R, SAS, SPSS, etc.), mathematical optimization, control theory, and time-series analysis. Professional working knowledge of Natural Language Processing, Information Retrieval, or Recommender Systems.
Role & responsibilities Essential Duties Include, but are not limited to, the following: Own productionizing modelsfrom tracked experiments to governed releases—ensuring resilient services with clear SLOs, runbooks, and fast, safe rollbacks. Build automation-first delivery: reproducible builds, layered tests, and environment promotion via GitLab CI and Terraform-based IaC. Engineer scalable serving: batch and real-time inference on EKS/ECS/Lambda and Databricks Model Serving with probes, autoscaling, and canary/blue-green deployments. Instrument end-to-end observability (data, model, system); detect drift/regressions; lead incidents and post-mortems that drive durable fixes. Partner across teams to translate requirements into designs, ADRs, and change plans; balance security, privacy, cost, and performance tradeoffs. Continuously reduce toil through automation, optimize model/GPU/LLM cost, and evolve templates/playbooks for repeatable delivery. Minimum Qualifications: Bachelor’s degree in Computer Science, Engineering, Data Science, or a related field and 3+ years of relevant experience as outlined in the essential duties; or High School Diploma/General Education Degree and 6+ years of relevant experience as outlined in the essential duties in lieu of Bachelor’s Degree. 3+ years operating ML systems in production (MLOps). Experience with Python for ML engineering (packaging, typing, testing, performance) Experience developing GitLab CI for ML/GenAI (multi-stage pipelines, artifacts, evaluation/security gates) and Terraform for ML/GenAI (reusable modules, drift detection); secure packaging & containerization. Experience deploying and operating compute for ML (EKS/ECS/Lambda), and secure data access patterns (S3/VPC/IAM/KMS, private endpoints) Experience implementing MLflow tracking, model registry & governed promotion, packaging & deployment to multi-target runtimes. Experience operating real-time + batch/streaming inference workloads, ML observability, layered testing (unit/integration), workflow orchestration, and cost optimization. Experience designing and implementing IAM least-privilege, secrets/key management for CI/CD pipelines; privacy and compliance awareness. Preferred Qualifications: Advanced GitLab CI (dynamic child pipelines, components, cross-project triggers, security scans, compliance gates). Advanced Terraform (policy-as-code, gated plan/apply, environment promotion). Advanced real-time serving (multi-tenant routing, dynamic model loading) and SLO-driven rollback/automation. Databricks governance (Unity Catalog, lineage) and feature platform approval/reuse workflows.
Role & responsibilities ## Essential Duties - Design and execute machine learning experiments to evaluate emerging AI technologies and frameworks. - Prototype and assess end-to-end AI solutions to inform product and platform strategy. - Formulate hypotheses and conduct structured evaluations to compare technical approaches. - Apply modern ML engineering practices to build and test scalable, modular proof-of-concept systems. - Contribute to the definition of best practices for experimentation, evaluation, and technical decision-making. - Synthesize and communicate experimental results to guide investment and adoption decisions. - Translate new research ideas and tools into functional, decision-relevant demonstrations. - Operate independently while contributing to a highly collaborative team environment. - Communicate technical findings clearly and concisely to both technical and non-technical audiences. ## Minimum Qualifications - Bachelors degree in computer science, data science, information technology, statistics, economics, or a related STEM field. - 3+ years of experience building machine learning-powered applications or tooling. - Strong proficiency in Python and familiarity with modern ML/AI libraries (e.g., PyTorch, Hugging Face, OpenAI SDKs). - Experience working with LLM orchestration or agent frameworks. - Understanding of model tuning, prompt engineering, or retrieval-augmented generation (RAG) patterns. - Experience developing and deploying applications in cloud environments (e.g., AWS, GCP, or Azure), including use of Docker and/or Kubernetes. - Demonstrated ability to independently prototype, test, and iterate on technical ideas. - Familiarity with ML evaluation techniques and structured experimentation workflows. - Proficiency with version control systems, CI/CD practices, and ML observability tools.