• Build and enhance AI-powered SaaS applications and internal platforms. • Translate founder ideas, workflows, and storyboards into production-ready product features. • Develop and maintain frontend and backend systems across multiple SaaS products. • Integrate third-party APIs and external services. • Develop AI-powered workflows using modern LLM technologies.

Role Description If you're a full-stack engineer who is already using tools like Claude Code, Codex, Cursor, or similar AI-assisted workflows to ship real product work — and you care enough to review, test, and secure every line those tools help produce — this is a chance to build meaningful software in a regulated, mission-critical industry. You'll join a PE-backed B2B SaaS company modernizing aviation compliance, credentialing, training, and access-control workflows used by airports and aviation organizations. This is a hands-on full-stack engineering role for an early-to-mid-career engineer who wants ownership, mentorship, and a clear path to grow. The company is moving legacy acquired products toward a newer unified platform while building new capabilities in areas like: - Drug program workflows - Fingerprinting - Background checks - Credentialing - Compliance operations The work is practical, high-impact, and directly tied to reducing key-person risk while accelerating a major platform transformation. You'll work closely with a hands-on Head of Engineering who wants to mentor engineers, raise the technical bar, and build a modern AI-forward engineering culture. What You'll Own - Build and ship full-stack product features - Develop new product capabilities across full-stack web applications - Work primarily in modern JavaScript/TypeScript environments, with React and Next.js strongly preferred - Help migrate important workflows from legacy systems into a newer platform architecture - Build features that support regulated aviation workflows such as compliance tracking, credentialing, background checks, fingerprinting, and related operational processes - Use AI-assisted development responsibly - Review AI-generated code for correctness, maintainability, security, edge cases, and bugs - Validate outputs through thoughtful testing, manual review, and clear technical reasoning - Contribute to team execution and reliability - Communicate progress, questions, and blockers proactively - Work with teammates to improve code quality, documentation, testing practices, and delivery flow - Help create a more energized, collaborative engineering team after a period of change What Makes You a Strong Fit - You have strong full-stack engineering fundamentals and enjoy solving product problems, not just writing code - You have experience shipping features using AI-assisted or agentic development workflows such as Claude Code, Codex, Cursor, or similar tools - You can explain how you validate AI-generated code before it reaches production - You are comfortable with JavaScript or TypeScript; TypeScript, React, and Next.js are especially relevant to the new platform work - You care about clean, maintainable code and understand the importance of testing - You are coachable, direct, and comfortable receiving feedback from senior engineering leadership - You can work with urgency while still being careful in regulated, compliance-oriented product domains - You are motivated by visible impact in a small-to-mid-sized company environment Experience Level - This role is open to a range of early-to-mid-level engineers - Roughly 3–5 years of software engineering experience is a strong fit - Candidates with 1–2 years of experience or strong recent graduate backgrounds may also be considered if they show strong technical signal, hunger, coachability, and shipped project work - Nonlinear career paths, startups, project-based work, or recent transitions are not automatic red flags, but you should be able to clearly explain what you built, why you moved, and what will make you successful here Why This Role Is Interesting - You'll work on real software for real customers in a regulated industry where accuracy and reliability matter - You'll be part of a platform modernization effort, not just incremental feature maintenance - You'll get direct access to engineering leadership and a strong mentoring environment - You'll help shape how AI-assisted engineering is used responsibly inside a product team - You'll have room to make a visible impact in a PE-backed SaaS company with meaningful growth expectations Compensation, Location, and Work Model - Base salary range: $100,000–$145,000, based on experience and level - Remote role based in the United States - Full-time position - Interview process includes video interviews A Note on the Environment This is not a purely greenfield startup role. There is legacy complexity, regulated workflow complexity, and a real need for engineers who can execute with focus. The right person will be excited by that mix: modern AI-assisted development, practical full-stack product work, and the chance to help move a business-critical platform forward. If you're hungry to build, excited about the future of software development, and ready to use AI tools with real engineering discipline, we'd like to hear from you. Apply now and get a response within 24 hours.

• Architect GitLab's AI-native learning ecosystem, including adaptive learning paths, coaching agents, bots, intelligent recommendations, and automated content workflows. • Lead GitLab's company-wide AI fluency and enablement strategy in partnership with the Enterprise AI team, from baseline literacy through builder capability. • Embed AI fluency into onboarding, leadership development, and role-specific learning pathways. • Own the multi-year learning platform strategy and roadmap, including platform evaluations, migrations, integrations, and capability expansions. • Drive operational excellence across the Talent Management & Development team by managing the product roadmap, release schedule, intake processes, documentation, automations, and cross-functional coordination. • Partner with People Technology as the technical lead for Talent Development, translating learner and business needs into architecture briefs and co-building agents, workflows, and platform integrations. • Partner with People Analytics to define measurement infrastructure and dashboards for learning engagement, AI adoption, behavior change, capability growth, and program return on investment. • Lead global compliance training and vendor management, including audit readiness, negotiations, renewals, quarterly business reviews, budgets, adoption targets, and investment cases.

Role Description We are building Joblogic’s AI Agent Platform — a multi-tenant system for designing, versioning, evaluating, and running AI agents that work across email, voice, SMS, WhatsApp, and CRM channels on behalf of our customers. The platform is built on a LangGraph runtime with a full agent lifecycle: a prompt-driven agent builder, a tool and knowledge-base registry, human-in-the-loop review, an agent memory subsystem, and a rigorous evaluation harness backed by LangSmith and PromptFoo. We are looking for a mid-level AI/ML Engineer to help us design, build, and continuously improve these agents. In this role you will own agent behaviour end to end — crafting and engineering prompts, wiring up tools and retrieval, and, most importantly, building the evaluations and datasets that prove an agent is doing the right thing before and after it ships. You will also bring applied machine learning depth: working with our execution and conversation data, building models and analyses that make agents smarter, and using platforms such as Databricks or AWS SageMaker to train, track, and serve them. You will work closely with product, backend, and platform engineers, and your work will directly shape how tens of thousands of field-service businesses experience intelligent automation. Qualifications - Strong Python engineering skills, with experience building production services and clean, well-tested code. - Hands-on experience building LLM-powered applications or AI agents — for example with LangChain / LangGraph, the OpenAI / Anthropic APIs, or comparable frameworks. - Demonstrable prompt engineering skill: designing, iterating on, and versioning prompts. - Practical experience with LLM evaluation: building datasets, defining rubrics/metrics, running evals, and interpreting results. - A solid machine learning foundation: framing problems, feature engineering, training and evaluating models. - Hands-on experience with a modern ML platform — Databricks or AWS SageMaker (or equivalent). - Strong data analysis skills using Pandas, NumPy, and SQL. - Understanding of retrieval-augmented generation (RAG): embeddings, vector/hybrid search, chunking, and grounding/faithfulness. - Experience working with APIs, JSON schemas, and integrating third-party services from Python. - Familiarity with both SQL and NoSQL databases. - Committed to continuous learning, proactive problem-solving, and timely issue identification. - Strong communicator, experienced in collaborating with cross-functional teams using tools such as Jira and Slack. - Creative and innovative thinker, consistently contributing fresh ideas and solutions. Requirements - Build and improve AI agents — design agent behaviour on our LangGraph runtime, configure abilities, attach tools and knowledge bases, and take agents from draft through evaluation to production deployment. - Prompt engineering — author, version, and systematically improve system prompts. - Design and run evaluations — build datasets, author heuristic and LLM-judge rubrics, run offline evaluations and online scoring against live executions. - Retrieval & knowledge (RAG) — build and tune retrieval-augmented generation over our knowledge bases. - Tools & integrations — develop and integrate the tools agents call. - Data analysis — analyse execution traces, conversation transcripts, and outcome data. - Applied ML — build, train, evaluate, and deploy machine learning models. - MLOps — use platforms such as Databricks or AWS SageMaker for feature engineering, experiment tracking, model training, and serving. - Observability & quality — use tracing and monitoring to debug agent runs in production. - Collaborate & ship — work in a cross-functional team using tools such as Jira and Slack, write clear documentation, and ship iteratively. Benefits - Professional Working environment - Market Competitive Salary - Life Insurance & Medical Insurance (Including Family) - OPD - Provident Fund - Gym Facility - Maximum 45 Weekly Hours (Monday–Friday) - Remote Working (During Pandemic Situation) - Company trip - 29 Annual Leaves - 8 Sick & uncapped Compassionate Leaves (As per Company Policy)