AI​/ML Engineer

Benefits:

• 401(k) matching (up to 3%)
• Health insurance (Tech Possible pays 50% of premium)
• Opportunity for advancement
• Paid time off
• Paid holidays
• Training & development
• Optional benefits:
  Dental, Vision, Accident, Critical Illness, Short Term Disability, Life Insurance
• Access to Employee Assistance Program
• Company-provided health club membership
• Travel reimbursement
• Opportunities for professional development and certification support
• Supportive, team‑oriented work environment

The AI/ML Engineer is the core hands‑on builder on the AI team. This person writes the code that makes the AI features work — the voice agent prompts, the intent classifiers, the RAG pipelines, the evaluation harnesses, and the integrations that connect AI capabilities to business systems.

• Implement AI workflows, including workflow logic, intent classification, entity extraction, and multi‑step decision processes
• Build and tune LLM‑powered features using OpenAI, Anthropic, and other APIs — including prompt engineering, structured output parsing, and function calling
• Develop NLP/classification systems for routing
• Design and run model evaluations: build test sets, measure accuracy/latency/cost, iterate on prompts and model choices
• Tune confidence thresholds (e.g., 90%/70% auto‑vs‑human routing) and monitor drift in production
• Integrate AI and workflows with existing enterprise systems
• Implement observability for AI systems, including structured logging, prompt and pipeline tracing, and cost attribution per execution
• Collaborate with cross‑functional teams to operationalize and iterate on prompts
• Contribute to code review, technical design docs, and on‑call rotations
• Delivers responsive and professional customer support by diagnosing and resolving technical issues, communicating clearly with non‑technical users, and ensuring timely follow‑up to maintain high levels of customer satisfaction
• Keep up with rapidly evolving technologies, algorithms, and industry trends
• Work closely with business managers and other engineers to align AI strategies with company goals

• Strong experience and knowledge in Python and Type Script
• Hands‑on LLM API experience:
  Anthropic Claude, OpenAI, at least one other provider; function calling, JSON mode, streaming
• Prompt engineering as a systematic practice — not ad‑hoc wordsmithing, but versioned prompts with test sets and regression checks
• NLP fundamentals: intent classification, NER, embeddings, semantic search (pgvector, Pinecone)
• Create evaluation approaches that define meaningful success metrics, build test datasets, and track performance against client‑specific KPIs
• Model selection trade‑offs: when to use GPT‑5 vs. Claude Sonnet vs. Haiku, when to fine‑tune, when RAG is the right pattern

• STT/TTS integration:
  Deepgram Nova/Aura, Whisper, Eleven Labs
• Real‑time streaming audio handling — latency budgets, interruption handling, turn‑taking
• Twilio Voice API, SIP trunking concepts, WebRTC awareness

• Backend development: REST APIs, async patterns, message queues
• Databases: SQL (Postgre
  
  SQL, SQL Server), basic schema design; vector DB basics
• Cloud:
  Azure (AWS/GCP acceptable), containerization (Docker)
• Git, CI/CD, code review fundamentals

• MLOps tooling:
  Langfuse, MLflow, Weights & Biases
• Familiarity with model adaptation techniques, such as fine‑tuning (e.g., LoRA, supervised approaches)
• Experience with frameworks:
  Lang Chain, Llama Index, Pydantic AI — used judiciously, not as a crutch