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Automated Reasoning Jobs in New York (NOW HIRING)

Senior Staff Agentic AI Engineer

New York, NY · On-site +1

$116K - $157K/yr

... reasoning quality, hallucination rates, tool latency), and supporting oncall through agentassisted triage, automated rollback/rollforward, and humangated controls. - (15%) * Establish enterprise ...

Manager Payments

New Brunswick, NJ · On-site

$70 - $100/hr

Configuring and tuning automated fraud rules and payment checks to improve approval rates while ... Proficiency in English is required Mathematical Skills & Reasoning Ability: * Must be able to ...

Turn deployment debugging into an automated pipeline, not a runbook. Build and own the automation ... You're comfortable reasoning about failure modes at the firmware and silicon level, not just the ...

Manager Payments

New Brunswick, NJ · On-site

$60K - $100K/yr

Configuring and tuning automated fraud rules and payment checks to improve approval rates while ... Proficiency in English is required Mathematical Skills & Reasoning Ability: * Must be able to ...

Senior Platform Engineer

New York, NY · On-site

$114K - $157K/yr

... automated health checks, and the monitoring that catches problems before customers do. Inference ... Solid infrastructure-as-code skills - designing modules, managing state, reasoning about blast ...

Rengo AI - AI Engineer

New York, NY · On-site +1

$125K - $150K/yr

Design LLM pipelines that: * avoid hallucinated financial reasoning * produce structured ... Replace manual analyst workflows with automated intelligence systems * Work on one of the hardest ...

Agentic AI Engineer

New York, NY · On-site

$107K - $214K/yr

Evaluation harnesses and automated regression testing * Human-in-the-loop architectures * Confidence thresholds and escalation models * Causal or counterfactual reasoning * Go/Golang * AWS, including ...

Agentic AI Engineer

New York, NY · On-site

$107K - $214K/yr

Evaluation harnesses and automated regression testing * Human-in-the-loop architectures * Confidence thresholds and escalation models * Causal or counterfactual reasoning * Go/Golang * AWS, including ...

Evaluation harnesses and automated regression testing * Human-in-the-loop architectures * Confidence thresholds and escalation models * Causal or counterfactual reasoning * Go/Golang * AWS, including ...

You'll architect the automated version, build it, and then own it in production: tracking KPIs ... Use your judgment on the right tool for the job: agentic workflows when tasks require reasoning and ...

Employ automated testing and ensure your work is tested in an automated and repeatable manner ... Demonstrate strong logic and reasoning capabilities. * Deliver well-defined work items, clarifying ...

Showing results 41-60

Automated Reasoning information

What is automated reasoning?

Automated reasoning is a field of computer science and mathematical logic dedicated to understanding how reasoning can be automated using computers. It involves developing algorithms and software that allow computers to prove theorems, verify software and hardware systems, and solve logical problems. Automated reasoning is used in areas such as formal verification, artificial intelligence, and knowledge representation, helping to ensure systems behave as intended and are free of certain types of errors.

What are the key skills and qualifications needed to thrive as an automated reasoning engineer?

To thrive as an Automated Reasoning Engineer, you need a strong background in computer science, logic, and formal verification, often supported by an advanced degree in a related field. Familiarity with formal methods tools (such as SMT solvers, model checkers), programming languages like Python, C++, or OCaml, and experience with verification frameworks are typically important. Analytical thinking, problem-solving, and effective communication skills help engineers tackle complex proofs and collaborate with interdisciplinary teams. These skills are crucial for ensuring the reliability and correctness of software and hardware systems in safety-critical environments.

What are some common challenges faced by professionals working in automated reasoning roles?

Professionals in Automated Reasoning often encounter challenges such as handling highly complex logical problems, ensuring the scalability of reasoning algorithms, and integrating automated reasoning tools with existing systems. Collaborating with interdisciplinary teams—including software engineers, data scientists, and domain experts—can present communication hurdles, as explaining formal logic concepts to non-experts is sometimes necessary. Additionally, staying up-to-date with the latest research and advancements in theorem proving and formal verification is crucial for continued success in this rapidly evolving field.

What job categories do people searching Automated Reasoning jobs in New York look for?

The top searched job categories for Automated Reasoning jobs in New York are:

What cities in New York are hiring for Automated Reasoning jobs?

Cities in New York with the most Automated Reasoning job openings:

Infographic showing various Automated Reasoning job openings in New York as of August 2026, with employment types broken down into 75% Full Time, and 25% Contract. Highlights an 87% In-person, and 13% Remote job distribution.

Senior Vice President, AI/ML Software Engineer

BNY

New York, NY

Full-time

Posted 22 days ago


Job description

hackajob is collaborating with BNY to connect them with exceptional professionals for this role.

Senior Vice President AI/ML Software Engineer

At BNY, our culture allows us to run our company better and enables employees’ growth and success. As a leading global financial services company at the heart of the global financial system, we influence nearly 20% of the world’s investible assets. Every day, our teams harness cutting-edge AI and breakthrough technologies to collaborate with clients, driving transformative solutions that redefine industries and uplift communities worldwide.

Recognized as a top destination for innovators and champions of inclusion, BNY is where bold ideas meet advanced technology and exceptional talent. Together, we power the future of finance – and this is what #LifeAtBNY is all about. Join us and be part of something extraordinary.

We’re seeking a future team member for the role of Senior Vice President AI/ML Software Engineer  to lead the architecture and delivery of production-grade AI systems built on agentic frameworks, retrieval-augmented generation (RAG), and LLM orchestration. This is a hands-on technical leadership role responsible for a team of engineers building autonomous AI pipelines that extract, validate, and reason over complex unstructured documents. You will own the technical vision for a multi-agent ecosystem -- designing pipeline orchestration engines, embedding/vectorization strategies, knowledge retrieval systems, and AI-assisted code generation tooling. You will lead a VP-level engineer and a broader team of 4-8 developers. This role is in New York, NY

What Sets This Role Apart - You build the agent framework, not just configure one -- custom orchestration engine, not a LangChain wrapper - Production AI with real consequences -- extraction accuracy directly impacts financial operations - Full RAG ownership -- from raw OCR bytes through embedding, retrieval, and generation - Evaluation-driven culture -- golden-truth datasets, automated regression, measurable quality gates - Greenfield AI + enterprise integration -- build new AI-native systems that plug into established platforms

In this role, you'll have the opportunity to impact on our organization in the following ways:

Technical Leadership & Architecture 

Architect agentic AI systems: multi-agent orchestration, tool-use patterns, planning/reasoning loops, and autonomous decision chains - Design and evolve RAG infrastructure -- chunking strategies, embedding pipelines, vector store selection, retrieval ranking, and context window optimization - Define vectorization strategy: embedding model selection, dimensionality trade-offs, hybrid search (dense + sparse), and re-ranking approaches - Own the AI pipeline orchestration framework -- blocks, inlets/outlets, blackboards, memory stores, and content policy enforcement - Make build-vs-buy decisions across the AI toolchain (vector databases, agent frameworks, evaluation harnesses, model gateways) - Establish patterns for prompt engineering at scale: prompt versioning, chain-of-thought decomposition, few-shot management, and guardrails 

Agentic & RAG Systems 

Design multi-agent architectures with shared memory, blackboard patterns, and inter-agent communication protocols - Build autonomous extraction agents capable of planning, tool selection, self-correction, and validation - Implement knowledge graph construction from unstructured documents -- entity extraction, relationship mapping, and graph-based retrieval - Develop evaluation frameworks: retrieval precision/recall, extraction accuracy, agent task completion rates, and hallucination detection - Design feedback loops: human-in-the-loop correction, reinforcement from golden-truth datasets, and continuous prompt refinement 

Team Leadership 

Lead, mentor, and grow a team of 4-8 engineers (AI/ML, backend, full-stack) - Directly manage a VP-level AI engineer; provide technical guidance and career development - Drive architecture reviews, design sessions, and technical decision-making - Own sprint planning, technical backlog, and delivery commitments - Foster a culture of rapid experimentation balanced with production rigor 

Hands-On Engineering - 

Implement core agentic components: agent loops, tool registries, memory persistence, and reasoning traces - Build embedding pipelines -- document preprocessing, chunk boundary detection, metadata enrichment, and vector index management - Develop scoring and validation systems (Bayesian confidence, cross-agent consensus, golden-truth comparison) - Contribute to platform services (Java/Spring Boot) and AI service layer (Python/FastAPI) - Build AI-assisted developer tooling: code generation workflows, automated test generation, and intelligent code review 

Delivery & Operations 

Own CI/CD pipelines, containerized deployments, and environment promotion - Define observability: agent execution traces, token usage tracking, retrieval quality metrics, and pipeline telemetry - Manage schema evolution and data stores (relational + vector) - Coordinate cross-team dependencies with platform engineering, data engineering, and infrastructure 

To be successful in this role, we’re seeking the following: 

Bachelor's degree or Advanced degree  in computer science engineering or a related discipline, or equivalent work experience required.  10+ years of professional software engineering experience - 3+ years leading or technically mentoring engineering teams - Deep expertise in AI/ML systems: - LLM orchestration, prompt engineering, chain-of-thought reasoning - RAG architectures: chunking, embedding, retrieval, re-ranking, context assembly - Agentic patterns: ReAct, tool-use, planning loops, multi-agent coordination - Vector databases and embedding models (OpenAI embeddings, sentence-transformers, FAISS, Pinecone, Weaviate, or similar) - Strong Python (3.11+): FastAPI, async/await, Poetry, Pydantic, pytest - Solid Java experience: Java 21, Spring Boot 3.x, microservice architecture - Production AI delivery: not just prototypes -- systems handling real workloads with observability, error recovery, and audit trails - Document intelligence: OCR pipelines, NLP, structured extraction from unstructured text - Testing & evaluation: golden-truth validation, retrieval metrics (MRR, NDCG), extraction F1 scores, agent success rates - Enterprise architecture: API design, circuit breakers, caching, event-driven patterns 

Preferred Qualifications 

Experience building custom agent frameworks (not just using LangChain/CrewAI out-of-the-box) - Knowledge of graph-based retrieval -- knowledge graphs, graph RAG, entity-relationship extraction - Experience with code AI: AI-assisted development tools, code generation pipelines, automated refactoring - Familiarity with model fine-tuning, LoRA/QLoRA, or RLHF techniques - Exposure to evaluation-driven development -- automated prompt regression testing, A/B testing of retrieval strategies - Angular/TypeScript experience for full-stack visibility - Capital markets or financial services domain knowledge - Familiarity with enterprise AI governance: content policies, PII handling, data residency 

Technology Stack 

AI/Agentic- LLM orchestration, multi-agent systems, ReAct patterns, tool-use, autonomous pipelines 

RAG & Vectors- Embedding models, vector stores, hybrid search, re-ranking, chunk optimization 

LLM- Azure OpenAI, GPT-4o, enterprise model gateways, prompt versioning 

Python- Python 3.12/3.13, FastAPI, Poetry, Pydantic, async pipelines 

Java- Java 21, Spring Boot 3.x, Maven, Resilience4j, Hazelcast 

Frontend- Angular 19, TypeScript, D3.js, ECharts 

Database- Oracle, PostgreSQL, vector databases 

Infrastructure- Docker, GitLab CI/CD, Artifactory 

Observability- Agent traces, token tracking, retrieval quality metrics, audit pipelines 

Our Benefits and Rewards:

BNY offers highly competitive compensation, benefits, and wellbeing programs rooted in a strong culture of excellence and our pay-for-performance philosophy. We provide access to flexible global resources and tools for your life’s journey. Focus on your health, foster your personal resilience, and reach your financial goals as a valued member of our team, along with generous paid leaves, including paid volunteer time, that can support you and your family through moments that matter. 

BNY is an Equal Employment Opportunity/Affirmative Action Employer - Underrepresented racial and ethnic groups/Females/Individuals with Disabilities/Protected Veterans.

This position is at-will and the Company reserves the right to modify base salary (as well as any other discretionary payment or compensation) at any time, including for reasons related to individual performance, change in geographic location, Company or individual department/team performance, and market factors.

At BNY, our culture allows us to run our company better and enables employees’ growth and success. As a leading global financial services company at the heart of the global financial system, we influence nearly 20% of the world’s investible assets. Every day, our teams harness cutting-edge AI and breakthrough technologies to collaborate with clients, driving transformative solutions that redefine industries and uplift communities worldwide.

Recognized as a top destination for innovators, BNY is where bold ideas meet advanced technology and exceptional talent. Together, we power the future of finance – and this is what #LifeAtBNY is all about. Join us and be part of something extraordinary.