AI & LLM Developer — Senior

Location: Remote or Hybrid (if US Located) Employment Type: Contract — Full-Time Department: Engineering / Product Development Experience Level: Senior (5–8+ years) Reports To: Director of Engineering Role Overview We are seeking a highly skilled Senior AI & LLM Developer with deep, hands-on experience in training, fine-tuning, composing, and deploying Large Language Models.

In this role, you will architect and build our internal LLM infrastructure and LLM Composer platform—enabling the organization to create, customize, orchestrate, and integrate AI capabilities across our entire product suite.

You will work at the intersection of machine learning engineering, platform architecture, and product development, integrating intelligent AI capabilities into real-world applications spanning telemedicine, InsurTech, workflow automation, analytics, and decision-support tools.

This is a pivotal role with direct influence on our product roadmap and technology strategy.

Key Responsibilities Internal LLM Development & Composer Platform Design, build, and maintain the company’s internal LLM training and fine-tuning infrastructure from the ground up, including data pipelines, training orchestration, evaluation frameworks, and model versioning.

Architect and develop the LLM Composer—a modular platform for chaining, routing, and orchestrating multiple LLM capabilities (e.g., specialized models, RAG pipelines, agent workflows, tool-use chains) into unified, composable AI services.

Establish model governance processes including experiment tracking, A/B testing frameworks, model registries, and reproducible training pipelines.

Create internal documentation, training materials, and runbooks to enable cross-functional teams to leverage the LLM Composer and internal AI tools effectively.

Model Training, Fine-Tuning & Optimization Train, fine-tune, and optimize LLMs using custom, open-source (LLaMA, Mistral, Falcon, etc.), and commercial foundation models.

Build and manage data preprocessing, curation, and augmentation pipelines for domain-specific training data (insurance, healthcare, compliance).

Implement advanced techniques including RLHF, DPO, LoRA/QLoRA, PEFT, knowledge distillation, and constitutional AI alignment methods.

Optimize model performance for latency, accuracy, throughput, and cost—including quantization (GPTQ, AWQ, GGUF), pruning, and efficient serving strategies.

Design and implement comprehensive evaluation systems with both automated metrics and human-in-the-loop review processes.

Product Integration & API Development Integrate LLM capabilities into backend services, mobile applications, web platforms, and enterprise workflows across the full product portfolio.

Develop production-grade APIs for inference, embeddings, semantic search, knowledge-base interactions, conversational AI, and autonomous agent workflows.

Build and maintain RAG (Retrieval-Augmented Generation) systems with vector databases, hybrid search, and dynamic context management.

Implement guardrails, content moderation, prompt injection defenses, and output validation to ensure safe and reliable AI behavior in production.

Infrastructure, Deployment & Monitoring Collaborate with DevOps and Platform Engineering to deploy, scale, and manage models in AWS / Kubernetes environments using containerized inference serving (vLLM, TGI, Triton, or equivalent).

Implement end-to-end MLOps pipelines for continuous training, evaluation, and deployment (CT/CE/CD).

Build monitoring and observability systems for model drift, data quality, inference latency, token usage, cost tracking, and security auditing.

Ensure all AI systems comply with PHI/PII regulations (HIPAA, SOC 2), data residency requirements, and enterprise-grade AI governance standards.

Research, Innovation & Team Enablement Stay current with rapidly evolving AI research—evaluate and prototype new architectures, techniques, and tools (multi-modal models, mixture-of-experts, long-context methods, agentic frameworks, etc.).

Conduct internal knowledge-sharing sessions, brown bags, and technical workshops to upskill engineering and product teams on AI/LLM best practices.

Contribute to technical strategy and architecture decision records (ADRs) for AI adoption across the organization.

Required Skills & Qualifications 5–8+ years of professional experience in ML/AI engineering, with at least 2–3 years focused specifically on LLM development and deployment.

Strong proficiency in Python and ML frameworks: PyTorch (preferred), TensorFlow, JAX, or equivalent.

Hands-on experience with LLM tooling ecosystems: LangChain, LlamaIndex, Haystack, Semantic Kernel, CrewAI, AutoGen, or similar orchestration and agent frameworks.

Proven track record of training or fine-tuning LLMs, including experience with techniques such as LoRA, QLoRA, RLHF, DPO, PEFT, and instruction tuning.

Deep experience deploying AI/ML solutions in cloud environments (AWS strongly preferred; GCP/Azure acceptable), including GPU instance management and cost optimization.

Strong understanding of model serving infrastructure: vLLM, TGI (Text Generation Inference), NVIDIA Triton, BentoML, or similar high-performance inference frameworks.

Expertise with vector databases (Pinecone, Weaviate, Milvus, PGVector, Qdrant) and RAG pipeline architectures.

Experience building production-grade AI-powered APIs and microservices using FastAPI, gRPC, or equivalent.

Strong mathematical and algorithmic foundations in linear algebra, probability, optimization, and information theory.

Excellent communication skills with the ability to translate complex AI concepts for non-technical stakeholders.

Preferred Qualifications (Nice to Have) Experience building internal AI/ML platforms, model registries, or LLM composition/orchestration systems.

Hands-on experience with multi-modal models (vision-language models, OCR pipelines, document AI, speech/audio models).

Familiarity with MLOps tooling: Kubeflow, MLflow, Weights & Biases, DVC, or similar experiment tracking and pipeline management tools.

Experience with AI safety, alignment research, red-teaming, or adversarial evaluation of LLMs.

Background in InsurTech, HealthTech, or regulated industries with understanding of HIPAA, SOC 2, and compliance requirements for AI systems.

Experience with graph databases, knowledge graphs, or ontology-driven AI systems.

Contributions to open-source AI/ML projects or published research in relevant conferences (NeurIPS, ICML, ACL, EMNLP, etc.).

Technology Stack & Tools Category Technologies Languages Python, TypeScript/JavaScript, SQL, Bash ML/DL Frameworks PyTorch, Hugging Face Transformers, DeepSpeed, FSDP LLM Tooling LangChain, LlamaIndex, Haystack, CrewAI, AutoGen, Semantic Kernel Model Serving vLLM, TGI, NVIDIA Triton, BentoML, TorchServe Vector Databases Pinecone, Weaviate, Milvus, PGVector, Qdrant Cloud & Infra AWS (SageMaker, Bedrock, ECS/EKS), Kubernetes, Docker, Terraform MLOps MLflow, Weights & Biases, Kubeflow, DVC, GitHub Actions Data & Storage PostgreSQL, Redis, S3, Snowflake, Apache Kafka Monitoring Prometheus, Grafana, LangSmith, Datadog, custom dashboards What We Offer A high-impact, greenfield role with the autonomy to shape our AI platform from the ground up.

Direct collaboration with executive leadership, product, and engineering teams.

Opportunity to work across multiple product verticals—telemedicine, InsurTech, analytics, and automation.

Competitive contract compensation commensurate with experience.

Job Type: Contract Pay: From $4,000.00 per month Work Location: Remote