Data Engineer Junior - AI / ML

ABOUT THE ROLE:

As part of our technology team, you will own end-to-end delivery across data engineering, machine learning, and agentic AI, building the analytical and automation capabilities that power our clean energy platform and support data-driven decisions across the business. You will help translate those capabilities into client-facing tools that create direct value, and support AI pilot programs from proof-of-concept through to production.

What You Will Do

• Agentic AI & client tools: Design, build, and deploy serverless LLM-powered agents and MCP servers on AWS Lambda, integrating tool use, RAG, and multi-agent communication patterns; translate client requirements into working AI tools, demo and iterate based on feedback, and help scale pilots to production.
• Data pipelines: Build and maintain ELT pipelines in Snowflake using SQL, Snowpark Python, and modern ETL/ELT frameworks; design schemas, tasks, and streams for analytics workloads.
• Analytics & dashboards: Deliver dashboards and ad-hoc analyses that surface insights for client and internal stakeholders.
• Machine learning: Develop and validate supervised and unsupervised ML models (e.g., logistic regression, time series, SVMs, CNNs/RNNs); support feature engineering, model tuning, and deployment via Lambda or SageMaker.
• Cross-functional collaboration: Work directly with business teams to understand KPIs, translate requirements, and communicate technical outcomes clearly; operate within an Agile/SCRUM workflow to estimate, track, and close stories and issues independently.

WHAT WE ARE LOOKING FOR:

• Education: Bachelor's in Computer Science, Data Science, or a related field; or equivalent professional experience. Master's a plus.
• Experience: 1–3 years of relevant experience, including internships or substantial project work.
• Python & SQL: Proficiency in Python and SQL; production experience with Snowflake or Snowpark preferred.
• LLMs in production: Hands-on experience building with leading LLM APIs (e.g., GPT, Gemini, Mistral); understands tool use, context management, and prompt engineering.
• Agentic AI: Familiarity with agent architectures, MCP, RAG pipelines, and multi-agent coordination patterns.
• Cloud infrastructure: Experience deploying serverless workloads on at least one major cloud provider (AWS Lambda, Azure Functions, or Google Cloud Run); familiarity with managed services such as object storage, AI/ML APIs, or model hosting. Basic IaC exposure (CDK, SAM, Terraform, or Bicep) is a plus.
• ML fundamentals: Strong understanding of classification and regression models (e.g., logistic regression, decision trees, SVMs) and unsupervised techniques such as clustering and dimensionality reduction; familiarity with time series methods and deep learning architectures (CNNs/RNNs) is a plus.
• Communication: Able to present findings and demo tools to non-technical stakeholders.

NICE TO HAVE

• LangChain / Strands: Familiarity with orchestration and agent frameworks for building LLM applications and pipelines
• AWS CDK: Infrastructure-as-code experience for defining and deploying cloud resources in Python or TypeScript
• CI/CD basics: Exposure to automated testing, deployment pipelines, or GitHub Actions
• Streamlit: Ability to build lightweight internal tools and data apps for rapid prototyping
• LLM API advanced patterns: Deep familiarity with tool use, streaming, function calling, and structured outputs
• Vector databases: Experience with embeddings storage and retrieval (e.g., Pinecone, pgvector, Weaviate)
• Snowflake Cortex / ML features: Experience using Snowflake's native ML and AI capabilities for in-warehouse inference.

We are committed to fostering a diverse, inclusive, and equitable workplace where individuals from all backgrounds feel valued and empowered to contribute their unique perspectives. We strongly encourage applications from candidates of all genders, races, ethnicities, abilities, and experiences to join our team and help us build a culture of belonging.