AIML Engineer

Overview 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. Responsibilities 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 Required Qualifications Strong experience and knowledge in Python and TypeScript 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 tradeoffs: when to use GPT-5 vs. Claude Sonnet vs. Haiku, when to fine-tune, when RAG is the right pattern Voice AI STT/TTS integration: Deepgram Nova/Aura, Whisper, ElevenLabs Real-time streaming audio handling — latency budgets, interruption handling, turn-taking Twilio Voice API, SIP trunking concepts, WebRTC awareness Software Engineering Backend development: REST APIs, async patterns, message queues Databases: SQL (PostgreSQL, SQL Server), basic schema design; vector DB basics Cloud: Azure (AWS/GCP acceptable), containerization (Docker) Git, CI/CD, code review fundamentals Benefits Health Insurance – TechPossible will pay 50% of the employee’s premium 401k Contribution – TechPossible will match employee contribution up to 3% Optional benefits: Dental, Vision, Accident, Critical Illness, Short Term Disability, and Life Insurance. Paid Time Off Paid Holidays Access to Employee Assistance Program Company provided health club membership Travel reimbursement Opportunities for professional development and certification support Supportive, team-oriented work environment #J-18808-Ljbffr