AI/ML Engineer

The AI/ML Engineer is responsible for designing, developing, and implementing machine learning models and artificial intelligence solutions to solve complex problems, optimize processes, and enhance decision-making. They work closely with data scientists and software engineers to build scalable, efficient systems while leveraging advanced algorithms and large datasets.

• Design, develop, implement and use machine learning algorithms and models to address business challenges and opportunities, such as predictive analytics, natural language processing, computer vision and recommendation systems.

• Collect, clean, and preprocess large volumes of structured and unstructured data from various sources, ensuring data quality, integrity and relevance for model training and evaluation.

• Train, validate, and optimize machine learning models using state-of-the-art techniques and frameworks. Evaluate model performance, interpret results, and iterate on model design as needed.

• Extract, select, and engineer relevant features from raw data to improve model performance and generalization capabilities. Utilizes domain knowledge and data exploration techniques to identify informative features.

• Deploy machine learning models into production environments, integrating them with existing systems and applications. Implements scalable, efficient, and reliable solutions for real-time batch inference.

• Monitor model performance, reliability, and scalability in production environments, implementing automated monitoring and alerting systems to detect anomalies and performance degradation.

• Document technical designs, implementation details, and best practices for AI solutions.

• Collaborate with cross-functional teams to include data scientists, software engineers, product managers, and other stakeholders to understand requirements, prioritize projects and delivery impactful AI Solutions.

• Perform additional duties as assigned.

• May coach and provide guidance to less experienced professionals.

• May serve as a team or task lead.

• Works independently under general supervision

To qualify, you must meet these basic qualifications:

Required Skills

• Bachelor’s degree in relevant field and 5+ years of experience

• Analytical & Programming

• Strong Python (data manipulation, model development; libraries like Pandas, NumPy, scikit-learn).

• SQL proficiency (joins, window functions, performance-aware queries).

• Statistical foundations (probability, hypothesis testing, regression, experimental design/A-B testing).

• Data Modeling

• End-to-end ML workflow experience (feature engineering, training, validation, deployment, monitoring).

• Data wrangling & ETL/ELT (building reliable pipelines; handling messy, large datasets).

• Model evaluation (metrics selection, bias/variance trade-offs, error analysis).

• AI Integration w/ MLOps

• Hands-on API integration for AI services (e.g., calling model endpoints, building microservices).

• Production deployment of models (packaging, versioning, CI/CD for ML).

• Model monitoring (drift detection, performance tracking, retraining triggers).

• Cloud Platforms

• Experience with at least one major cloud (Azure, AWS, or GCP) for data/AI workloads.

• Familiarity with containers (Docker) and source control (Git).

• Data visualization skills (Power BI or Tableau) to communicate insights and outcomes.

• Communication

• System analysis skills to identify viable AI insertion points in processes, products, or workflows.

• Stakeholder communication (translating technical findings into business value and concrete recommendations).

• Documentation of models, assumptions, data lineage, and decisions.

• Governance/Security

• Responsible AI awareness (fairness, explainability, privacy, and compliance considerations).

• Basic understanding of data security and access controls in production environments.

Preferred Skills

• Advanced AI/LLM

• Experience with LLMs (e.g., Azure OpenAI Service/OpenAI API) for summarization, classification, or copilots.

• Prompt engineering and evaluation of LLM outputs for quality and safety.

• RAG pipelines (retrieval-augmented generation), vector databases (e.g., Azure AI Search, Pinecone, FAISS), and embeddings.

• Fine-tuning or model adaptation strategies for domain-specific use cases.

• MLOps Engineering

• Model orchestration/experiment tracking (MLflow, Weights & Biases).

• Kubernetes and ML deployment tools (e.g., AKS/EKS, Argo, KServe).

• Feature stores, A/B testing frameworks, and event-driven/streaming data (Kafka, Kinesis).

• CI/CD pipelines (GitHub Actions, Azure DevOps) and Infrastructure as Code (Terraform, Bicep).

• Data Platform Integration

• Databricks, Snowflake, or BigQuery experience.

• Building robust APIs (REST/GraphQL) and microservices around models.

• Monitoring & Observability (Prometheus, Grafana; app & model logs).

• Responsible AI & Compliance

• Practical experience with model risk management, documentation standards, and explainability (SHAP, LIME).

• Knowledge of privacy-by-design and PII handling (data minimization, anonymization).

• (If applicable to the environment) familiarity with FedRAMP or regulated environments.

• Additional Languages/Tools

• R, PySpark, or Scala for data-intensive workloads.

• LangChain or Semantic Kernel for LLM app development.

• Tableau/Power BI advanced (parameterized dashboards, Row-Level Security).

• Ability to support 24x7 environment for business critical and contractual SLA impacting issues

Clearance: Candidates must be eligible to obtain a federal security clearance