Generative AI Engineer. No 3rd partiesC&G Consulting Services Inc • Raritan, New Jersey, United States
Generative AI Engineer. No 3rd parties
C&G Consulting Services Inc • Raritan, New Jersey, United States
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job_description.job_card.job_descriptionApply large language models (LLMs) and NLP techniques to solve complex problems such as named-entity recognition, question answering, and summarization. Build fine-tuning pipelines using frameworks like LoRA / PEFT and run hyperparameter sweeps in Azure ML. Author multi-step prompt chains, enforce structured JSON outputs, and use validation guards to reduce hallucinations and improve model consistency. Develop and containerize agent-based microservices using frameworks like FastAPI or Azure Functions. Define Infrastructure as Code using Terraform / ARM and build CI / CD workflows in GitHub Actions for automated testing and canary rollouts. Implement robust monitoring and alerting for latency (p50 / p95) and error rates using tools like Prometheus, Grafana, or Azure Monitor to ensure SLA compliance. Profile API calls and implement cost-reduction strategies like batching, caching, and early-stop logits. Produce high-quality documentation, including architecture diagrams, sequence flows, and data schemas. Enforce security and compliance standards, including data encryption and PII redaction, to align with HIPAA / GxP requirements. BS / MS in Computer Science, AI / ML, or a related field. 3+ years of experience building end-to-end LLM / RAG systems in a production environment. Deep Python experience, including libraries like FastAPI, pandas, and NumPy. Hands-on experience with LLM orchestration frameworks (LangChain, LlamaIndex), NLP libraries (HuggingFace), and OpenAI / Azure SDKs. Proven expertise in MLOps including CI / CD (GitHub Actions / Azure DevOps) and containerization (Docker / Kubernetes). Experience working in a regulated industry such as pharmaceuticals or life sciences. Hands-on experience with vector databases like Milvus or Pinecone. Familiarity with chatbot frameworks like Rasa or Botpress. Experience with data-centric AI tools for validation and monitoring, such as Great Expectations or Deepchecks.
NO 3 Party candidates
Candidates must be direct
DO NOT RESPOND UNLESS YOU ARE DIRECT
Key Responsibilities :
High-Throughput RAG Pipeline Development :
- Design and build scalable document processing pipelines to ingest and semantically chunk large batches of documents (PDF / DOCX) from sources like Azure Blob and AWS S3.
- Integrate embedding models and tune vector databases like Milvus for high-performance, sub-100 ms k-NN retrieval.
- Implement hybrid retrieval systems using BM25 and vector search, and continually track and improve retrieval performance using metrics like MRR and recall@k.
Model Fine-Tuning & Prompt Engineering :
MLOps & Production Deployment :
Performance, Cost & Standards :
Required Qualifications :
Preferred Qualifications (Nice-to-Haves) :
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