Machine Learning Engineer V

Avalara APAC; India; On-site; INR 1,500,000 - 2,500,000; Full time

Benefits offered by this job

Health insurance

Paid time off

Paid parental leave

Bonuses

Job summary

Avalara APAC is seeking an experienced Engineer to lead the development of production AI systems that enhance compliance workflows. You will work on implementing state-of-the-art ML techniques and collaborate cross-functionally to drive AI capabilities.

The ideal candidate holds a Bachelor's degree in Computer Science, has over 8 years of relevant experience, and possesses strong skills in Python and cloud-native platforms. The role offers a dynamic environment promoting innovation and technical growth.

Qualifications

• 8+ years of experience in machine learning engineering, applied ML, NLP.
• Strong software engineering skills in Python.
• Experience shipping production ML or LLM systems.

Responsibilities

• Lead architecture and delivery of production-grade AI and ML systems.
• Mentor engineers and improve engineering standards.
• Drive improvements to classification and extraction systems.

Skills

Machine learning engineering

Experience with Python

Experience with NLP

Cloud-native platforms

Education

B.S. in Computer Science or related field

Tools

Kubernetes

AWS

Docker

GitLab

Job description

What You'll Do

Avalara is accelerating its AI‑first transformation by building intelligent systems that automate complex compliance workflows, understand unstructured and structured data, and enable product teams to deliver AI‑powered experiences at scale. We are investing in senior ML leadership to build the next generation of production AI capabilities.

This Engineer Will Help Avalara

• Lead development of production AI systems that solve complex classification, extraction, reasoning and automation problems
• Build reusable GenAI and ML platform capabilities that help product teams ship AI features faster
• Advance state‑of‑the‑art document intelligence for ingestion, extraction, validation and structured understanding
• Improve the quality, reliability, observability, security and cost efficiency of LLM and ML workloads
• Establish stronger evaluation, governance and production‑readiness standards for AI systems
• Raise the technical bar across architecture, system design, mentoring and operational ownership

Key Responsibilities

• Lead architecture and delivery of production‑grade AI and ML systems across classification, document intelligence, retrieval and GenAI platform components
• Design and scale GenAI platform components such as RAG systems, agent workflows, prompt systems, evaluation pipelines, document ingestion services and developer‑facing APIs
• Build and optimize ML and LLM‑powered services with strong reliability, latency, observability, security and cost controls
• Work across model providers and families to optimize model selection, routing, latency, quality and spend
• Develop reusable SDKs, APIs, workflows and platform components that enable teams to ship AI features faster
• Lead evaluation and governance mechanisms for AI quality, grounding, safety, explainability, compliance and production risk management
• Drive improvements to classification, extraction, retrieval and decisioning systems using ML, SLMs, LLMs, embeddings, and agent/tool‑use patterns
• Partner with product, application teams, infrastructure, security and domain experts to onboard new use cases and data sources
• Mentor engineers, lead design reviews, improve engineering standards and raise the team’s execution quality

12‑Month Success Signals

• Delivered production‑grade AI platform or ML capabilities adopted by multiple product or engineering teams
• Improved quality, reliability, latency, cost efficiency or observability of important ML or LLM workloads
• Established reusable architecture patterns for GenAI, classification, document intelligence, evaluation or model serving
• Enabled faster onboarding of new AI use cases, data sources or product workflows
• Improved governance and evaluation standards for AI‑powered systems
• Led at least one technically complex AI initiative from ambiguity through production delivery
• Demonstrated clear impact as a technical mentor and raised the quality of architecture, design reviews, implementation and operational ownership

AI Expectations

• Apply advanced AI techniques such as LLMs, SLMs, RAG, embeddings, transformer models, agentic workflows, tool use and document intelligence
• Use AI to materially improve speed, quality, automation, insight, scale, reliability or cost efficiency
• Make strong architectural tradeoffs across accuracy, latency, reliability, security, governance and spend
• Design production‑grade evaluation systems for AI quality, grounding, regression testing, safety and failure analysis
• Identify meaningful AI opportunities tied to product value, operational efficiency, customer experience and risk reduction
• Share patterns, tools and best practices that elevate AI capability across teams
• Apply AI responsibly, securely and ethically in enterprise‑grade production systems

Bar Raiser Expectations

We are looking for engineers who raise the technical quality and production maturity of AI and ML systems around them. In this role, that means demonstrating strong ownership, sound judgment and measurable impact across model development, evaluation, deployment and ongoing improvement. The ideal candidate uses data, experiments and production signals to guide technical decisions; understands the tradeoffs between accuracy, latency, reliability, cost and maintainability; and brings rigor to how models are evaluated, monitored and improved over time. They help raise the team’s AI/ML capability by improving experimentation practices, model evaluation standards, code quality, documentation, observability and operational readiness. They also contribute reusable patterns that make future AI systems easier to build, safer to operate and more scalable in production.

Qualifications

• B.S. in Computer Science, Engineering or related technical field
• 8+ years of experience in machine learning engineering, applied ML, NLP, GenAI, information retrieval or production AI systems
• Strong software engineering skills in Python and experience designing backend services, APIs, distributed systems or cloud‑native platforms
• Experience shipping production ML or LLM systems, not just experimentation or research prototypes
• Hands‑on experience with modern AI techniques such as RAG, embeddings, prompt engineering, evaluation, classification, agent/tool‑use patterns, SLMs or LLM‑powered workflows
• Experience with cloud‑native systems, APIs, async or distributed workflows, observability, monitoring and production operations
• Ability to design for security, access control, governance, compliance, reliability and cost efficiency
• Strong system design skills and ability to lead technically complex initiatives across multiple stakeholders
• Ability to mentor engineers and raise technical standards across teams

Preferred Qualifications

• Experience with multi‑model gateways, model routing, model selection or model cost optimization
• Experience with document AI, OCR, extraction, layout understanding or validation workflows
• Experience with evaluation frameworks for LLMs, agents, classification systems or retrieval systems
• Experience building internal AI platforms, developer tooling, SDKs, self‑serve portals or shared ML infrastructure
• Experience with Kubernetes, Docker, MLflow, Terraform, AWS, GitLab, Postgres, Prometheus or Grafana
• Experience in domains with complex taxonomies, compliance requirements, long‑tail data or high reliability expectations

Benefits

Total Rewards

In addition to a great compensation package, paid time off and paid parental leave, many Avalara employees are eligible for bonuses.

Health & Wellness

Benefits vary by location but generally include private medical, life and disability insurance.

Inclusive Culture and Diversity

Avalara strongly supports diversity, equity, and inclusion and is committed to integrating them into our business practices and our organizational culture. We also have a total of 8 employee‑run resource groups, each with senior leadership and executive sponsorship.

Equal Opportunity Employer

Supporting diversity and inclusion is a cornerstone of our company – we don’t want people to fit into our culture, but to enrich it. All qualified candidates will receive consideration for employment without regard to race, color, creed, religion, age, gender, national orientation, disability, sexual orientation, US Veteran status, or any other factor protected by law. If you require any reasonable adjustments during the recruitment process, please let us know.