Postúlate en Kit Empleo: kitempleo.com.ar/empleo/q1meh

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;

Postúlate en Kit Empleo: kitempleo.com.ar/empleo/q1meh

📌 Senior Ai Software Developer (Tandil)
🏢 Net2Source
📍 Tandil