Senior AI Engineer
- Posted a month ago
- Be among the first 10 applicants
Job Description
Responsibilities
- Design, develop, and deploy large language model (LLM) pipelines, incorporating foundational models, retrieval-augmented generation (RAG), memory and caching mechanisms, and external API integrations.
- Implement generative AI solutions tailored to the needs of regulated industries such as financial services and healthcare.
- Apply advanced natural language processing (NLP) techniques and frameworks to address complex language understanding and generation challenges.
- Deploy, manage, and scale AI solutions on cloud platforms (AWS, Azure, GCP).
- Conduct risk assessments for GenAI pipelines, addressing data privacy and security concerns, bias and fairness in model outputs, hallucination and factual accuracy issues, prompt injection vulnerabilities and toxicity, and model drift and long-term performance degradation.
- Develop safeguards, monitoring systems, and compliance frameworks to ensure responsible and regulatory-aligned AI deployment.
- Collaborate with cross-functional teams, including product, compliance, engineering, and data science, to deliver business-ready AI solutions.
- Proven expertise in developing and deploying large language models (LLMs) and generative AI solutions.
- Proficiency in Python and widely adopted AI/ML libraries, including Hugging Face Transformers, LangChain, CrewAI, Sklearn, Plotly, TRL, and FastAPI.
- Experience with agent development platforms and SDKs, such as OpenAI Agent SDK, Google Agent SDK, Dialogflow, VAPI, Salesforce AgentForce, and Microsoft Copilot.
- Master's degree in computer science, data science, or a related field.
- Minimum 5 years of experience in developing and deploying AI/ML solutions.
- At least 3 years of recent, hands-on experience in NLP and generative AI projects.
- Proven track record of delivering successful AI initiatives in regulated industries, preferably financial services or healthcare.
- Strong problem-solving and analytical thinking abilities.
- Excellent communication skills for articulating AI concepts to diverse stakeholders.
- Ability to collaborate effectively across cross-functional teams.
- Adaptability to rapidly evolving AI technologies and industry standards.
- Domain Knowledge: Healthcare is preferred.
- Understanding of healthcare and life sciences regulatory frameworks (HIPAA, GDPR).
- Awareness of generative AI use cases in healthcare, including patient engagement and virtual assistants, medical documentation assistance, scheduling, and operational automation.
More Info
Job Type:
Industry:
Employment Type:
Key Skills
About Company
Skills:
Llama), LLMs (GPT, Weaviate), vector databases (FAISS, Orchestration, Docker, Python, AWS, React, Gcp, MLops, Kubernetes, Prompt engineering, multi-agent orchestration, tool-using AI agents, Azure OpenAI, RAG pipeline development, Plan-and-Execute, Multi-Agent Systems, Pinecone, LangChain, CrewAI, Generative AI, Agentic AI development, Claude, LLMOps, Semantic Kernel, AutoGen, CI/CD
Skills:
Apis, Docker, Cloud Infrastructure, Python, LLM tool calling, RAG embeddings, LLM-powered applications, vector search and retrieval strategies, scalable architectures
Skills:
Jenkins, Typescript, Python, Test Architecture, GitHub Actions, Playwright, AI coding tools, LLM experience, prompt engineering, context retrieval agents
Skills:
cloud security, Azure ML, Azure, Python, LLM evaluation guardrails, AI GenAI development, Azure OpenAI, Semantic Kernel, LangGraph, AutoGen, ML fundamentals, cost management, LlamaIndex
Skills:
Sqlalchemy, API security, PostgreSQL, Rabbitmq, Sqlite, Flask, FastAPI, Python, LangChain, Django ORM, AI ML development, document extraction, langFuse, large-scale RAG systems, Cloud Data Warehouses, OCR pipelines, LangGraph, RAG methodologies, langSmith, Java Spring Boot, fine-tuning LLMs