Junior AI Developer

PURPOSE OF POSITION

Assist with model integration, data pipelines, retrieval infrastructure, and the engineering scaffolding required to ship reliable, secure, and cost-effective Artificial Intelligence (AI) features. This role ensures the delivery of production-grade Large Language Model (LLM) systems that meet real-world demands for performance, cost-efficiency, and governance.

MINIMUM QUALIFICATIONS

Education: Bachelor's Degree in Computer Science, Data Science, AI, or related field is preferred, but not required. Equivalent practical experience, including boot camps, certifications, or self-directed learning, is also valued.

Training and Experience: 0-2 years of professional experience in software development, data engineering, machine learning, or backend development.

General Skills: Must have strong software engineering fundamentals and a deep understanding of working with LLMs in production environments. The ideal candidate brings hands-on experience with Python and modern data tooling and is comfortable building robust pipelines that connect unstructured content, structured data, and retrieval systems to power context-aware LLM workflows. You should demonstrate fluency in the design and reasoning of data movement processes, including ingestion, preprocessing, vector indexing, and query generation. Experience working with both open-weight and API-based large language models is also essential. This role requires a practical mindset, a strong command of SQL and retrieval strategies over relational data, and the ability to experiment, evaluate, and iterate toward scalable, cost-effective, and trustworthy AI features.

Required Skills:

• Proficiency in Python, including experience with modern practices in structuring, testing, and maintaining codebases.

• Experience with Retrieval-Augmented Generation (RAG) systems, including document chunking, embedding, vector search, and grounded context construction.

• Hands-on experience with PostgreSQL and pgvector, including schema design and structured retrieval over relational data.

• Strong familiarity with SQL query generation, particularly in the context of semantic or hybrid retrieval.

• Experience integrating and orchestrating LLMs, with a focus on prompt templating, tool usage, and response parsing.

• Familiarity with Google ADK or equivalent frameworks for LLM scaffolding and orchestration.

• Comfort working with unstructured and structured data, including ingestion from PDFs, DOCX, Markdown, HTML, and APIs.

• Experience deploying and debugging LLM systems, including containerization (Docker), API-based LLM integration (e.g., Ollama or vLLM), and environment configuration.

Preferred Skills

• Experience with graph-enhanced retrieval, using tools like Neo4j or ArangoDB, and an understanding of when and how to apply knowledge graphs to improve LLM grounding.

• Knowledge of model adaptation techniques, including LoRA, QLoRA, or PEFT approaches for fine-tuning or personalization.

• Familiarity with prompt optimization strategies, including prompt evaluation and failure case analysis.

• Basic understanding of hybrid search and reranking pipelines, such as ColBERT, BGE rerankers, or commercial tools like Cohere Rerank.

• Experience with infrastructure optimizations, such as autoscaling (KEDA, HPA), Redis caching layers, or efficient streaming and batching.

• Familiarity with safe deployment practices, including prompt injection mitigation and handling of sensitive or regulated data.

Clearance: Must be able to obtain/maintain a Secret clearance. Prefer holds an active Secret clearance.

DUTIES & RESPONSIBILITIES

• Design and implement end-to-end RAG architectures, including document ingestion, chunking, embedding generation, vector indexing, query planning, retrieval, and response synthesis.

• Evaluate and integrate LLMs, embedding models, and vector databases to support efficient and accurate retrieval and generation.

• Design and implement scaffolding and orchestration around LLMs, including prompt templating, tool invocation, evaluation harnesses, and safety guards.

• Develop data processing pipelines for structured and unstructured content (PDF, DOCX, HTML, Markdown, databases, APIs); implement normalization, deduplication, PII redaction, and metadata enrichment.

• Implement and optimize retrieval strategies and context construction (citation, source attribution, grounding).

• Adapt retrieval and embedding strategies to domain-specific taxonomies, ontologies, or structured schemas; support contextual retrieval from hierarchical or relational sources.

• Productionize LLM-based systems: containerize components (Docker), deploy orchestration via Kubernetes or serverless platforms, implement observability (OpenTelemetry, logging, tracing), and manage configuration.

• Measure and improve quality: define offline and online evals, golden datasets, A/B tests, hallucination detection, toxicity filters, and guardrails.

• Optimize performance and cost: batching, caching, streaming, and efficient context management.

• Implement security, privacy, and compliance best practices including access controls, injection defense, and safe data handling.

• Develop solutions that can run entirely on-premise or in air-gapped environments, prioritizing data sovereignty and privacy.

• Various other duties in direct support of accomplishment of primary duties listed.

SUPERVISORY/MANAGEMENT RESPONSIBILITY

None