Senior AI Software Developer

Responsibilities: Solution Engineering & Delivery Translate high-level designs into clear component contracts, APIs, and service boundaries. Implement LLM integrations, RAG pipelines, agents, tool/function calling, and prompt strategies. Own feature delivery for sprints/releases; maintain high code quality and documentation.

Modeling & Evaluation Fine-tune models when needed; design evaluation harnesses and metrics. Build A/B testing setups; track accuracy, latency, robustness, and task success rates. Conduct error analysis; iterate using feedback efficacy loops and prompt refinement.

Data & Retrieval Engineering Build ETL/ELT pipelines; curate datasets with metadata, lineage, and validation. Implement vector indexing (chunking, embeddings, reranking), tune chunk size & overlap. Enforce data governance: PII handling, redaction, consent, auditability.

MLOps & Platform Readiness Containerize workloads (Docker); orchestrate deployments (Kubernetes/Helm). Own CI/CD for ML: train → evaluate → package → deploy → monitor → rollback. Maintain model/agent registries, experiment tracking, and reproducible environments.

Software Engineering & Integration Build microservices and async inference paths; support batch/stream processing. Integrate with enterprise auth, observability, telemetry, and logging. Write unit/integration/ee tests, performance benchmarks, and failure-injection tests.

Observability, Reliability & Performance Instrument with metrics/logs/traces; define SLOs (latency, throughput, error rate). Optimize inference: batching, caching (KV cache), quantization, token efficiency. Implement guardrails (safety filters, jailbreak detection), auto-evals and alerts.

Security & Compliance Apply secure coding practices; manage secrets, encryption, and least privilege. Ensure compliance (data residency, consent, audit trails); respect IP policies. Enforce policy-based access and content safety in user-facing features.

Collaboration & Mentoring Review designs/PRs; coach L engineers on best practices. Coordinate with AI Architects, Data Engineers, QA, and Product. Education and Experience Required: Bachelor's or master's degree in computer science, engineering, data science, machine learning, artificial intelligence, or closely related quantitative discipline.

Typically, - years' experience. Knowledge and Skills: LLMs & Agents: Prompt engineering, function/tool calling, orchestration frameworks, RAG. ML/DS: Evaluation metrics (precision/recall, BLEU/ROUGE where relevant), error analysis.

Data/RAG: Embeddings, similarity (cosine/IP), chunking, rerankers, vector DB operations. Backend: Python (FastAPI/Flask), microservices patterns. MLOps/Infra: Docker, Kubernetes, CI/CD, artifact management, GPU scheduling.

Observability: Metrics/logging/tracing, dashboards, automated evaluation pipelines. Frameworks: PyTorch/TensorFlow, Hugging Face, LangChain/LlamaIndex. Data: Pandas, SQL/NoSQL, Parquet/Arrow, Kafka/queues.

Vector DBs: FAISS, Milvus, pgvector, Pinecone, Weaviate. Ops: GitHub Actions/Azure DevOps, MLFlow/W&B