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/e2e 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 L3 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, 7-10 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