---
title: 'AI Engineer at Onit, Inc.'
canonical: 'https://feeny.ai/job/ai-engineer-onit-inc-pune-nwxsg17n2r4x'
type: 'job'
last_seen: '2026-11-06'
---

# AI Engineer at Onit, Inc.

- **Company:** Onit, Inc.
- **Location:** Pune, India
- **Employment:** full-time
- **Work type:** hybrid
- **Posted:** 2026-10-28
- **Last confirmed live:** 2026-11-06
- **Apply:** https://jobs.lever.co/onit/a442529d-7dc7-4f89-963c-0a5f63240a9c

## Job description

## About Onit

We're redefining the future of legal operations through the power of AI. Our cutting-edge platform streamlines enterprise legal management, matter management, spend management and contract lifecycle processes, transforming manual workflows into intelligent, automated solutions.

We’re a team of innovators using AI at the core to help legal departments become faster, smarter, and more strategic. As we continue to grow and expand the capabilities of our new AI-centric platform, we’re looking for bold thinkers and builders who are excited to shape the next chapter of legal tech. If you're energized by meaningful work, love solving complex problems, and want to help modernize how legal teams operate, we’d love to meet you.

Agentic AI Feature & Workflow Development

- Build and integrate AI-driven features using LLM APIs (OpenAI / Azure OpenAI, Anthropic, Gemini on Vertex AI)
- Design and implement tool-using agents (structured function calling, schema validation, retries, fallbacks)
- Build multi-agent workflows when appropriate (e.g., planner/worker, reviewer/critic, specialist routing) and know when a simpler architecture is better
- Create agentic workflows such as document understanding, extraction, reasoning over evidence, task automation, and multi-step decision support
- Own context engineering end-to-end:
- dynamic context assembly (retrieval + state + tool outputs)
- context budgeting and compression/summarization
- grounding strategies to reduce hallucinations and improve consistency
- Implement retrieval-augmented generation (RAG) and search workflows using off-the-shelf vector stores and embedding services

Evaluation, Quality & Iteration (Core)

- Establish evaluation frameworks for accuracy, reliability, and output quality
- Build task-specific eval suites: golden datasets, adversarial cases, regression tests, and rubric-based scoring
- Set up automated evaluation pipelines and release gates (CI/CD-friendly) tied to prompt/model/version changes
- Define and monitor online metrics (e.g., task success rate, human override rate, safety flags, latency, cost) and run experiments/A-B tests where appropriate
- Use LLM-as-judge responsibly: calibrate, validate, and pair with human labels when needed

Engineering, Integration & Observability

- Develop scalable backend services and APIs that incorporate AI functionality
- Integrate AI pipelines into existing cloud, microservices, and event-driven architectures
- Implement observability and analytics for all AI features (tracing, evaluations, prompt versioning, cost tracking) Example tooling: Langfuse (and/or OpenTelemetry-compatible stacks)
- Ensure reliability, uptime, performance, and security of AI services
- Build internal tooling for evaluation, testing, prompt/version management, and safe deployment

Product & Collaboration

- Partner with product managers, designers, the Chief Architect, and domain SMEs to shape AI-first solutions
- Rapidly prototype concepts and iterate based on user feedback and measurable eval results
- Translate business problems into well-structured AI workflows without requiring ML model training
- Document system behavior, known failure modes, and operational playbooks

Governance & Safety

- Implement guardrails, checks, and fallback logic for safe and predictable AI behavior
- Help define and follow compliance, privacy, and responsible AI guidelines
- Design for safe tool execution (bounded actions, permissions, escalation paths, human-in the-loop review

Core Strengths (Required)

- Strong software engineering background (Python preferred) and experience shipping backend services
- Deep hands-on experience building agentic LLM systems from first principles: agent loops, tool interfaces, planning/replanning, memory/state, and failure handling
- Strong context engineering ability: retrieval strategies, routing, grounding, context budgeting, and long-context tradeoffs
- Strong evaluation discipline: golden datasets, regression gating, automated eval pipelines, and online monitoring
- Practical experience with LLM APIs (OpenAI/Azure OpenAI/Anthropic/Gemini) and AI orchestration frameworks
- Excellent debugging, systems thinking, and problem decomposition skills
- Comfortable operating in fast-paced, ambiguous environments with high ownership

Signals We Value

- You’ve shipped an LLM/agent system in production and can clearly explain:
- the failure modes you discovered
- the evals you built to catch regressions
- how you improved cost/latency while increasing quality
- how you monitored and iterated safely over time
- You keep up with industry developments (model releases, frameworks, best practices) and can translate them into pragmatic improvement

## Nice to Have

- Experience with cloud platforms (AWS and/or GCP), microservices, and event-driven systems
- Experience with observability stacks (OpenTelemetry, Datadog, Honeycomb) and AI-specific tooling (e.g., Langfuse, Braintrust, HumanLoop, MLFlow)
- Experience with workflow orchestration for long-running jobs (Temporal, Celery, Airflow)
- Experience building enterprise AI features (permissions, auditability, compliance constraints)
- Experience with safety/policy layers (PII handling, prompt injection defenses, sandboxed tool execution)

## Why Join Us

- Build core AI capabilities that directly impact users and product strategy
- Work on cutting-edge, real-world agentic systems—focused on applied engineering (no model training required)
- High ownership, fast iteration cycles, and strong cross-functional collaboration
- Competitive compensation and opportunities for rapid advancement

What Your First 90 Days Could Look Like

Ship one production agent workflow end-to-end with:

- tracing + observability
- an offline eval suite with regression gates
- cost/latency targets and monitoring
- documented failure modes and fallback path

Benefits & Perks That Support You: Onit offers a comprehensive total rewards package designed to support the whole employee at work and beyond: Health Coverage: Employee and immediate family members. Time Away: Flexible paid time off and 10 company paid holidays annually. Family Support: Exceptional paid leave for birth parents, non-birth parents, and caregivers.  Onit also offers surrogacy and adoption reimbursement. Income Protection: 100% employer-paid life and disability insurance. Additional Coverage Options: Voluntary benefits including hospital indemnity, critical illness, accident. Tax-Advantaged Accounts: Flexi, NPS. Community Engagement: One paid volunteer day each year to give back to the community.

Our Commitment to Applicants We know that not everyone will check every box in a job description. At Onit, we value diversity, inclusion, and authenticity. If you’re excited about this role but your experience doesn’t align perfectly with every qualification, we encourage you to apply. You may be exactly who we’re looking for.

Onit Values Customer First - Customer success is our success. We deliver value, listen, and act on customer needs. Purposeful Innovation - Innovation fuels our growth. We harness creativity to solve problems and lead with the intentions and expertise. Win as One - Teamwork is how we win. We are accountable, act with integrity, and communicate openly. Intentional Growth - Our people are the difference. We create an environment with compelling work, impactful contributions, and career growth.

## About Onit, Inc.

## Company Overview
- **One-liner**: Onit is an AI-native legal operations platform that helps legal and compliance teams manage workflows, contracts, matters, and spend.
- **Entity Type**: Private (late‑stage; $216.6M total funding over 11 rounds)
- **Headquarters**: Atlanta, Georgia, United States
- **Founded**: 2009 (company site) – some sources list 2011 (LinkedIn); conflicting reports exist.
- **Founders**: Not publicly disclosed

## Core Business
- **Primary Industry**: Legal technology (legal operations software)
- **Target Customers**: B2B, primarily legal and compliance departments in enterprise, Fortune 500, and high‑growth companies
- **Mission / Purpose**: To give legal teams the freedom, flexibility, and technology to run their departments on their own terms, transforming legal business processes with purpose‑built, AI‑native solutions.

## Products & Services
- **Enterprise Legal Management (ELM)**: Full‑lifecycle matter, spend, and vendor management – SaaS platform
- **Contract Lifecycle Management (CLM)**: AI‑powered contract creation, review, negotiation, and storage
- **Intake & Workfloow Automation**: Configurable service‑request portals and approval chains
- **AI Agents & Assistant**: Generative‑AI tools that automate contract redlining, clause policy checks, and answers from corporate policies

All products are unified under a single, adaptable framework.

## Market Standing
- **Valuation / Market Cap**: Not publicly disclosed
- **Key Metric**: Annual revenue ~$78.3M (LinkedIn estimate) – note: this is a third‑party estimate, not official.
- **Total Funding**: $216.6M (11 rounds; latest round not specified)
- **Notable Investors / Partners**: Not disclosed in available sources (investor names not found)
- **Growth Signals**: 3,000+ customers worldwide; 389 employees (+13.3% YoY); LinkedIn followers 63,694 (+50.3% YoY); multiple acquisitions; rapid scaling of AI‑native products.

## Competitive Advantages
- **AI‑native Architecture**: Built from the ground up to leverage AI, not bolted‑on.
- **Domain Expertise**: Created by legal professionals for legal teams – deep understanding of legal workfloows.
- **Configurability**: Highly adaptable platform that allows legal departments to shape the software to their processes.
- **Scale & Trust**: Trusted by over 3,000 customers including Fortune 500 enterprises – demonstrated retention and market penetration.

## Strategic Focus
- **AI‑First Innovation**: Continuing to embed AI agents and generative capabilities across the platform.
- **Acquisitions**: Using M&A to broaden product portfolio and accelerate growth.
- **Global Expansion**: Growing presence in key regions (New Zealand, Ukaine, Uk, India) while maintaining a remote‑first culture.
- **Customer Success**: Deepening relationships and driving value through automation and insights.

## Why Work Here
- **Culture Highlights**: Recognized as a *Most Loved Workplace*; values include Customers First, Purposeful Innovation, Win as One, and Intentional Growth.
- **Remote / Hybrid**: Remote work is embraced, monitored by performance not micro‑management. Office hubs in Atlanta, Pune, New Zealand, Ukaine, and United Kingdom.
- **Benefits**:
  - 24‑wekks paid leave for all new parents (adoption/surrogacy included) from Day 1
  - Company‑wide bonus plans (minimum 6% for all levels)
  - Quarterly performance reviews focused on growth dialogue
  - Multiple internal collaboration platforms (AwardCo, The Wave, intranet, townhalls, AMAs)
- **Engineering Culture**: Emphasis on modern tech stack (Microsoft‑centric, Python, Ruby on Rails), AI engineering roles visible – Senior AI Engineer, Lead Software Engineer – Python, AI Engineer, Senior Software Engineer – RoR.

## Sources
1. [onit.com/careers](https://www.onit.com/careers/); culture, benefits, and values  
2. [onit.com/about](https://www.onit.com/about-onit/); company overview, history, mission, products  
3. [linkedin.com/company/onit](https://www.linkedin.com/company/onit/); employee count, revenue estimate, founding year, funding  
4. [mostlovedworkplace.com/companies/onit](https://mostlovedworkplace.com/companies/onit/); benefits, remote policy, values, awards  
5. [onit.com/leadership](https://www.onit.com/leadership/); leadership mention (Farlekas)

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