AI/ML Full-Stack Engineer

Role Overview
Build AI-powered applications with a true full-stack mindset: back-end services, conversational/NLP, data pipelines, and a front-end (React/TypeScript) when needed. You’ll train/fine-tune/deploy models, craft RAG pipelines, expose secure APIs, and integrate with enterprise systems. We do not require a specific GPA. We value real production work, design rigor, and measurable results.

• Non-traditional paths (bootcamps, self-taught, OSS track record) are welcome if you demonstrate competence via portfolios and interviews.
• Publications, patents, or conference talks are pluses, not requirements.

Key Responsibilities
AI/ML Development

• Build, fine-tune, evaluate, and deploy ML/LLM models for domain use cases.
• Apply NLP techniques: classification, summarization, intent/entity, embeddings, sentiment, conversational flows.
• Implement prompt templates, tool-use schemas, and evaluation sets; measure accuracy/latency/cost.
• Works in a squad led by a Solution Architect; participates in design reviews and writes ADRs.
•  Owns features end-to-end: RAG pipeline, service/API, minimal front-end (React/TypeScript) when needed, plus tests and dashboards.
• Mentors junior engineers; contributes to runbooks, on-call rotation, and post-incident reviews.
   

Conversational AI

• Design end-to-end conversational workflows; optional use of Rasa/Dialogflow/Botpress or custom pipelines.
• Integrate with enterprise apps; handle context, memory, and fallbacks.

Backend & API Development

• Design/deploy RESTful/microservices for AI endpoints using FastAPI/Flask/Django (Python) or Express.js/Spring Boot/Laravel.
• Implement secure auth (OAuth2/OIDC), role-based access, token scopes; build asynchronous, event-driven workloads with queues/schedulers.

Data Engineering & Pipelines

• Automate ingestion/cleaning/chunking/feature engineering; manage structured/unstructured data.
• Use scraping frameworks (Playwright/Puppeteer/Scrapy/BS4) where needed.
• Operate across SQL (Postgres/MySQL) and NoSQL (Mongo, Elastic, Dynamo).

Cloud & DevOps

• Deploy on AWS/Azure/GCP with autoscaling/monitoring; containerize with Docker/Kubernetes.
• Set up CI/CD for models and services; add monitoring, retraining triggers, and drift/perf tuning.

Required Experience

• 5-10+ years in software/solutions architecture; 4+ years delivering ML/NLP or conversational systems in production.
• Proven team leadership of cross-functional squads (planning, estimation, delivery, coaching).
• Built agentic or tool-using assistants and RAG pipelines at scale; strong grasp of retrieval quality vs. latency trade-offs.
• Deep backend (Python + one of Node/Java), microservices, messaging (Kafka/Rabbit/SQS), resiliency (retries, circuit breakers).
• AI/ML: PyTorch/TensorFlow, HuggingFace, embeddings/vector search; evals & prompt techniques.
• Data/Stores: SQL + NoSQL (Postgres/MySQL, Mongo/Elastic/Dynamo).
• Cloud/DevOps: AWS/Azure/GCP, Docker/K8s, CI/CD, observability (Prometheus/Grafana/OpenTelemetry), MLflow (or similar).
• Security/compliance literacy (PII handling, RBAC/least privilege, audit evidence).
• Strong hands-on with PyTorch/TensorFlow, HuggingFace; building and serving models.
• Solid backend engineering in Python plus one of Node.js/Java/PHP; microservices and API design.
• Comfortable across data pipelines, SQL/NoSQL, and production troubleshooting.
• Cloud, containers, CI/CD; observability basics (logs/metrics/traces).
• Excellent debugging & problem-solving; pragmatic about tools.

Nice to Have

• LLM fine-tuning and prompt engineering; event-driven architectures (Kafka/Rabbit/SQS).
• Observability for ML systems (Prometheus/Grafana/MLflow).
• Multi-cloud/serverless experience.

Minimum (one of the following):

• Bachelor’s in Computer Science, Electrical/Computer Engineering, Mathematics, Data Science, or related; or
• Bachelor’s in another STEM field plus strong software experience; or
• Equivalent industry experience (5–10+ yrs) building production AI/ML + backend systems.

Preferred:

• Master’s in CS/AI/ML/Data Science (thesis or capstone in NLP/LLM/RAG is a plus).
• Formal coursework or certifications in Machine Learning, Deep Learning, NLP, Data Structures & Algorithms, Databases, Distributed Systems.

Evidence we look for (can substitute for formal degrees):

• Production projects: RAG pipelines, LLM apps with tool use, APIs in FastAPI/Express/Spring, CI/CD.
• Open-source contributions (Hugging Face, LangChain, vector DBs, retrievers) or published tech blogs/papers.
• Kaggle/benchmarks or internal evals with clear metrics (accuracy/latency/cost).

Relevant certifications (nice-to-have, not mandatory):

• Cloud: AWS Developer/Architect, Azure Developer/AI Engineer, Google Professional ML Engineer.
• MLOps/Platform: Kubernetes (CKA/CKAD), Terraform, MLflow/Vertex/Azure ML badges.
• Data/Streaming: Databricks, Snowflake, Kafka.
• Security: AZ-500 / AWS Security / ISC² CC for basics.