Sr. Agentic AI, AI/ML Engineer — New York City Metropolitan Area

• Must be a U.S. Citizen or Green Card holder. FULL-TIME ONLY, NO CONSULTANTS

• ONLY local candidates from NYC, NJ, CT and must interview in-person

• 5 day work week is a must

• Seeking smart, hardworking candidates from top 40 colleges/universities in USA.

• Compensation: $250,000–$315,000 Base Salary + Performance Bonus + RSUs

Job Title: Senior Agentic AI, AI/ML Engineers

Employment Type: Full-Time

Work Location: Midtown East, NEW YORK CITY

Job Description

We are looking to hire multiple top-tier Senior Agentic AI Engineers for a leading technology-driven company that develops advanced AI platforms for traders and portfolio managers .

The ideal candidates will have strong hands-on engineering experience, particularly in Python programming, Artificial Intelligence, and Machine Learning . ectively.

Must-Have Requirements

• Strong hands-on Python programming experience – Mandatory.

• Extensive experience in Agentic AI, Artificial Intelligence and Machine Learning

• Combination of skills below:

1. Advanced LLM Orchestration & Frameworks

• Agent Framework Mastery: Deep experience with frameworks designed for multi-step reasoning and loops, such as LangChain, LlamaIndex, AutoGen, or CrewAI.

• Prompt Engineering for Autonomy: Crafting robust system prompts, few-shot examples, and chain-of-thought (CoT) structures that guide agents to self-correct and debug their own outputs.

• State Management: Managing conversational and execution state across long-running, asynchronous agent loops to prevent context degradation and memory loss.

2. Planning, Reasoning & Decision-Making Paradigms

• Architectural Patterns: Implementing advanced design patterns like Reflection (self-critique), Tool Use (Function Calling), Planning (breaking complex goals into sub-tasks), and Multi-Agent Collaboration.

• Reasoning Frameworks: Familiarity with approaches like ReAct (Reason + Act) and Tree-of-Thoughts (ToT) to help agents dynamically choose the best next step.

3. Tool Integration & API Engineering

• Robust Tool Use: Designing reliable interfaces for agents to interact with external systems, databases, internal enterprise tools, and web APIs.

• Structured Output Parsing: Utilizing tools like Pydantic or native JSON mode to ensure agents communicate with downstream code reliably without breaking due to malformed LLM responses.

4. Retrieval Systems & Memory Management

• Advanced RAG (Retrieval-Augmented Generation): Implementing semantic search, hybrid search, reranking models, and graph-based knowledge retrieval (GraphRAG) to feed accurate context to agents.

• Vector Databases: Proficiency with vector stores (such as Pinecone, Milvus, Qdrant, or pgvector) for efficient context storage and retrieval.

• Memory Tiers: Architecting short-term working memory (context window management) and long-term memory (episodic and semantic storage) for stateful agents.

5. Evaluation, Safety & Guardrails

• Agent Evaluation: Developing automated evaluation pipelines (using frameworks like Phoenix or TruLens) to measure agent success rates, loop efficiency, and failure modes across thousands of scenarios.

• Guardrails & Security: Implementing safety frameworks (like NeMo Guardrails) to prevent prompt injection, rogue looping, unauthorized tool execution, and toxic generation.