Requirements:
10+ years of software engineering experience with a strong backend background.
3+ years as a Tech Lead or technical leader in fast-paced environments.
Strong AI engineering skills, including context engineering, agent optimization, troubleshooting, and systematic evaluation of AI system performance.
Excellent system design and distributed systems experience.
Strong understanding of scalable cloud architectures (AWS or equivalent).
Experience building production-grade backend services and microservices.
Excellent communication and stakeholder management skills.
Passion for solving complex technical problems.
Curiosity and excitement about AI and rapidly evolving technologies.
Ability to lead technical direction while remaining hands-on.

Requirements:
Must have:
8+ years engineering experience with production systems, including time leading or tech-leading a team.
Experience building a team from the ground up - first hires, culture, hiring bar.
Shipped AI/LLM products to production - not just demos or POCs.
Strong Python and/or TypeScript.
Deep hands-on experience with LLMs, agent frameworks (LangGraph, Mastra, AWS Strands, Vercel AI SDK, or similar), prompt engineering, RAG, and model behavior in production.
Distributed systems fundamentals: workflow orchestration, fault-tolerant architectures, async patterns.
Cloud and self-hosted model deployment (Bedrock, Vertex AI, Azure OpenAI, Anthropic, Ollama).
Hands-on leadership - you write code, review PRs, set the bar. You also know when to step back.
Comfortable with ambiguity and the pace of a zero-to-one build.
Nice to have:
Background in network security, asset discovery, or traffic analysis
Familiarity with OT/ICS network protocols (Modbus, S7comm, PROFINET, DNP3)
Multi-agent architectures in production (A2A, agent swarms).
Memory libraries (Mem0, LangMem, MemGPT).
LLM evaluation frameworks (LangSmith, Bedrock Evaluations).
Vector stores (Pinecone, Weaviate, Chroma), PostgreSQL/MongoDB.
Background in cybersecurity or critical infrastructure.
IaC (CDK, Terraform).

Requirements:
Significant experience building complex software systems - backend, cloud, distributed systems, data platforms or enterprise architecture
Several years leading engineering organizations through managers and senior technical leaders across multiple teams
A track record of scaling an org while preserving technical quality, speed, accountability and the talent bar
Hands-on experience building or operating AI / ML / LLM / agentic systems in production
Real understanding of what makes AI agents reliable: evaluations, orchestration, context, model behavior, observability, permissions, security, failure handling
Credible contribution to architecture without becoming the approval bottleneck
Management treated as a craft: hiring, coaching, feedback, performance management, org design
Strong product and business judgment, and clear communication with engineers, executives and customers
Comfort in an early-stage environment where product, org and category are still being shaped

Requirements:
7+ years of experience as a Backend Developer / Data Engineer / ML Engineer
3+ years in a technical leadership role.
Python (Java as an advantage)
Bachelors degree in Computer Science or related STEM field (Masters preferred).
Proven track record of building and deploying AI-based solutions at scale.
Deep expertise with LLMs and ML frameworks (e.g., LangChain, LangGraph, Hugging Face, TensorFlow, PyTorch).
Strong background in system design, cloud-native architecture, and microservices.
Experience with NoSQL and real-time data processing pipelines.
Exceptional leadership, mentorship, and communication skills.
Strategic mindset with the ability to balance hands-on coding and team leadership.

Requirements:
You've shipped agentic systems to production - real orchestration, tool use, structured outputs, and the failure modes that only appear at scale. Not "I've called an LLM API."
You've built the evaluation discipline, not just consumed it: trajectory tests, golden datasets, regression gates, offline replay. "It seems better" is not a metric, and you have opinions about what is.
Deep backend and distributed-systems engineering. Strong Python, and comfort with workflow orchestration (Temporal or equivalent), streaming, and cloud-native infrastructure. Agent platforms are systems problems wearing an AI hat.
Fluency across the modern agent stack - LangChain/LangGraph-style frameworks, multi-provider routing, structured output contracts, prompt and context engineering - with the judgment to know which parts are load-bearing and which are fashion.
Security literacy. Enough to reason about whether an agent's security output can be trusted, and to argue with researchers on the merits. You don't need to be a vulnerability researcher.
Influence without authority. You'll change how three teams work with no one reporting to you. Show us where you've done that.
Advantage: experience with AI/LLM security - red-teaming agents, prompt injection, or agentic attack patterns.
Advantage: background in cybersecurity, detection engineering, or WAF/mitigation systems.
Advantage: you've driven AI adoption across a whole company, not only an engineering org.

Requirements:
Strong software engineering background with 6+ years of hands-on Python development experience is required. The ability to read, debug, and reason about C++, Rust, or Go is a significant advantage
Deep understanding of distributed systems: concurrency, consistency models, fault tolerance, and large-scale system behavior under stress
Background in one or more of: storage systems, networking (TCP/IP, RDMA), cloud infrastructure, database internals, or high-performance backend systems
Experience building large-scale infrastructure platforms, internal developer platforms, or reliability engineering systems
Leadership:
Proven track record leading complex technical initiatives from architecture through delivery
Experience mentoring and growing engineers - raising the technical bar of a team, not just directing work
Ability to drive technical alignment across teams, communicate tradeoffs clearly, and make high-quality architectural decisions at speed
Comfortable operating at both the strategic and hands-on level - you write code, review designs, and shape roadmaps
Previous experience in people management roles - Advantage
Mindset:
You approach quality through the lens of Site Reliability Engineering: you care about MTTD, observability, and building self-healing systems
You have a "hacker" instinct - you don't just find bugs; you find the architectural flaws that allowed them to exist
You are an early adopter of AI tools and excited about applying LLMs and generative AI to accelerate engineering velocity
Big Advantages
Experience with storage systems, file systems, or high-performance distributed environments
Background in chaos engineering, fault injection, or simulation systems
Familiarity with observability tooling and performance engineering at scale
Experience building testing or reliability platforms as first-class engineering products
Prior experience as a Team Lead in a high-growth infrastructure company

Requirements:
5+ years of experience building backend systems and distributed architectures.
3+ years of experience in a technical leadership or team lead role, managing and mentoring engineers.
Strong hands-on experience with Golang (or other backend languages with a willingness to work in Go)-an advantage
Experience designing and operating large scale cloud native systems.
Strong experience with Major cloud platforms - GCP is an advantage.
Hands-on experience with Kubernetes and containerized microservices architectures.
Experience building event driven systems and streaming pipelines using Kafka or similar technologies.
Strong understanding of system design, scalability, performance optimization, and reliability engineering.
Experience working with large scale data processing systems.
Experience working with and early adapting to new AI tools.
Background in cybersecurity, SaaS security, or data protection technologies - an advantage.
Background in SaaS applciation API usage and throttling - an advantage
Excellent leadership, communication, and problem solving skills.
Ability to thrive in fast moving environments with high ownership and autonomy.
Believe in learning new things every day.

Requirements:
7+ years of experience in backend and frontend development, including at least 4 years in a team leadership role
Strong backend development experience with Node.js, Python, and TypeScript
3+ years of frontend development experience with React
Hands-on experience with AWS services such as Batch, S3, SNS, Lambda, DynamoDB, and RDS, including deployment and automation workflows
Proven experience designing and developing robust RESTful APIs
Strong understanding of SQL and NoSQL databases, including performance optimization and scalability best practices
Experience with cloud-native technologies, including AWS, Docker, Kubernetes, and Serverless architectures
Proven track record of delivering large-scale, high-quality products
Experience developing or integrating AI-powered product capabilities
Outstanding interpersonal and communication skills
Demonstrated ability to lead and mentor engineering teams, manage complex projects, and collaborate effectively across cross-functional teams
Advantage:

Previous experience in a fast-paced startup, ideally within a SaaS company, and familiarity with the unique challenges of scaling a product.
Experience with Model Context Protocol (MCP) or similar agent/tooling frameworks
Experience working on infrastructure or core platform teams

Requirements:
Your Impact
Design and implement automated evaluation loops, static analysis, and rigorous quality gates to ensure the ADLC process doesn't just write code, but consistently produces great, production-ready code.
Help the team tackle complex, hard problems to elevate our autonomous development product from "good" to "excellent".
Lead complex initiatives in Context Engineering and Prompt Engineering.
Manage and orchestrate the complex ecosystem of autonomous agents utilized for internal development.
Serve as a leading individual in a very strong team professionally and personally - Were looking for someone who not only delivers his own work but improves that of those around them.
Find space for growth to push the entire team or group forward - New projects, changing processes or improving existing tools.
View prompt engineering as a core engineering discipline-where rewriting agent behavior is a versioned, reviewed, and tested code change.
Act with a debugging temperament; conduct deep-dive analyses of raw agent transcripts to diagnose non-deterministic failures and ascertain root causes instead of merely working around them.
Your Experience
At least 8+ years of experience in software development, architecture, or owning operational systems in production.
Computer Science B.Sc. or equivalent education or equivalent military experience required.
A product builder's mindset: you can extract requirements, talk to stakeholders, and tell the difference between what's important and what's noise.
Experience in building production grade agents. Deep understanding of the agent loop, its states and transitions. You know how to build it correctly, not just use it.
Positive can-do mindset, able to work independently and within a team.
Hands-on experience with LLM APIs, including a practical, highly-skeptical understanding of token costs, caching, context windows, and model failure points.
You know how to build the right context for a task, including memory systems, session storage, and vector databases.
You understand where LLMs fail and how to design around those failure points.
You've used traces or observability tooling to diagnose and improve agent behavior.
A systems-level background that touches reliability, observability, or platform engineering, with a strong preference for writing narrow, deterministic code over building hypothetical abstractions.
Experience in the cybersecurity space - an advantage.

Requirements:
6+ years in Data Engineering, including production-scale platform design and operation using Spark
2+ years as a Tech Lead or senior engineer leading technical initiatives
Hands-on backend development experience - REST/gRPC APIs, microservices, service design patterns - at enterprise scale
Broad database fluency: relational, analytical, NoSQL, columnar, and streaming stores
Advanced SQL and strong Python; proven experience building and optimizing large-scale ETL/ELT pipelines
Experience with cloud-based data platforms and AI-driven data workflows
Practical proficiency with AI agents and tools - building, integrating, or orchestrating LLM-powered workflows in production environments
Strong communication skills with technical and non-technical stakeholders
Self-directed and forward-thinking - you bring a clear point of view, drive initiatives without being told, and see around corners on where the platform needs to go next
Nice to have
Azure data services, Databricks or EventHub
Streaming and real-time data processing architectures
Background in cyber security, SaaS, or large-scale enterprise environments
Data governance, observability, or self-service platform experience.