Forward Deployed Engineer, Transformation at Kinaxis – Chennai | Jobyssey

Forward Deployed Engineer, Transformation

Location IN-TN-Perungudi, Chennai

ID 2026-34911

Category Engineering

Position Type Full-Time

Remote No

About Kinaxis

Are you looking to join an innovative, market-leading company where you can truly elevate your career? At Kinaxis we are serious about culture, we are serious about technology, we are serious about customers, and we are serious about not taking ourselves too seriously. If you are looking to be part of an incredible growth story, then we might just be the place for you!

In 1984, we started out as a team of three engineers. Today, we have grown to become a global organization with over 2000 employees around the world, 6 global office and a best-in-class HQ in Ottawa, Canada. As winners of several Top Employer awards globally, we are proud to work with our customers and employees towards solving some of the biggest challenges facing supply chains today.

Kinaxis is a global leader in modern supply chain orchestration, powering complex global supply chains, and supporting the people who manage them. Our powerful, AI infused platform provides full transparency and visibility across end-to-end supply chains, enabling our customers to make faster, better decisions. We are trusted by renowned global brands to provide the agility and predictability needed to navigate today’s volatility and disruption. With more than 40,000 users in over 100 countries, we are expanding our team as we continue to innovate and revolutionize how we support our customers.

Location

Chennai / Bangalore - India

Work Model - Hybrid

About the role

Kinaxis is building a Forward Deployed Engineers team whose singular mission is to Help customer transform how work gets done in the Agentic Age. This is not a consulting role, and it is not a features team. It is a Multi deployment unit embedded, operating with the speed and ownership culture of a startup.

This team operates as a POD inside Kinaxis — a small, tight unit with end-to-end ownership and a startup mentality inside a larger company. We are scrappy, resourceful, and self-directed. We move fast, trade tasks to whoever is closest to the need, and stay relentlessly focused on customer impact.

You will work shoulder-to-shoulder with customers — from warehouse floor to executive sponsor — designing, building, and shipping Agentic AI solutions on the Kinaxis Maestro Platform. When a solution works, you abstract it into a reusable pattern. When something breaks in production, you own the fix. You operate in a POD team: tight, multi-disciplinary, and self-directed.

💡 Attitude is the biggest asset. We hire for trajectory and mindset, not years on a resume. The right bias to action, ownership, and appetite to keep learning cannot be taught — but every technical skill here can be. Bring those, and we will invest in the rest.

What you will do

Build the data backbone for AI Agents on Maestro

Design, build, and operate production data pipelines — ingestion, transformation, serving, and monitoring — that feed agentic AI workflows on the Kinaxis Maestro Platform.
Integrate customer data sources (ERP systems, data warehouses, supply chain applications, flat files, APIs) into a unified data layer that agents can reason over reliably.
Build and maintain data platform services, tooling, and infrastructure that enable reliable data ingestion, processing, sharing, and discovery across customer deployments.
Own data quality end-to-end: profiling, validation, anomaly detection, and lineage — because an agent is only as good as the data behind it.

Ship AI Agents alongside the POD

Partner with AI-focused FDEs to design, develop, and ship agentic AI workflows — including multi-agent orchestration, RAG pipelines, and tool-use patterns — ensuring the data layer is production-ready.
Translate messy, real-world customer data landscapes into clean, well-modeled foundations that agents can consume — iterating rapidly from prototype to deployment.
Troubleshoot complex production issues across the full stack data pipelines, model context, API layers, and infrastructure — in live customer environments.

Own the full solution lifecycle

Carry multiple customer engagements in parallel; set expectations early, renegotiate honestly when priorities shift, and own outcomes end-to-end.
Define and track success in business terms: data freshness SLAs, pipeline reliability, query performance, exception rates — not just deployment completion.
Drive platform engineering best practices through Infrastructure-as-Code, CI/CD automation, observability, and automated testing across customer deployments.

Work inside a POD team

Operate as part of a small, self-directed POD within Kinaxis tasks traded fluidly to whoever has the right skills, focus maintained with urgency.
Partner closely with Product and Engineering to convert field learnings and repeatable data patterns into durable platform capabilities, not one-off scripts.
Serve as the technical face of the team in customer rooms — from IC-level engineers to skeptical architects to VP sponsors and build genuine trust across all of them.

Drive adoption and change

Guide customer data teams through solution transitions: the new data architecture has to stick, not just ship.
Mentor customer teams on modern data engineering practices, agentic data workflows, and new operating patterns so capability transfers and does not remain dependent on you.

What we are looking for

Bias to action

You move on what matters. You finish what you start. The outcome is yours — no waiting for perfect clarity.

Self-directed

You identify what matters, secure the help or learning you need, and deliver — without being told.

Always learning

Every customer, teammate, and new tool is a chance to level up. Not knowing something is fine; refusing to learn it is not.

Customer-obsessed

You can sit with a frustrated VP, a skeptical architect, or a junior analyst — and leave each one feeling heard, helped, and impressed.

Comfortable with ambiguity

Undefined problem, undefined scope, undefined success metric — and you still ship.

Drives change, not just code

You guide customers through transitions and mentor their teams to adopt new ways of working.

The following are the technical areas we look for. You do not need depth in all of them — we look for strong foundations, a growth trajectory, and the ability to pick up what is missing quickly.

Data Engineering & Platforms (Primary)

Strong hands-on experience building and operating production data pipelines — ingestion, transformation, serving, and monitoring — using tools such as Spark, Airflow, Kafka, dbt, or equivalents.
Deep working knowledge of modern data platforms (Databricks, Snowflake, BigQuery, or similar), including Unity Catalog, Delta Lake, data modeling, query optimization, and performance tuning at scale.
Strong SQL skills and deep understanding of data modeling, schema design, and large-scale data processing patterns.
Experience with data governance practices: RBAC, PII handling, data lineage, data quality frameworks, and enterprise-scale data management.
Hands-on experience with cloud platforms (Azure, GCP, or AWS) and Infrastructure-as-Code tools (Terraform, Pulumi, or similar).
Strong debugging instincts with imperfect, real-world data in production environments — you have untangled broken pipelines at 2 AM and lived to tell the tale.

AI & Agentic Development

Hands-on experience building LLM-based applications — RAG systems, multi-agent workflows, tool-use patterns, prompt and context engineering.
Familiarity with modern AI-assisted development tools (Copilot, Claude Code, agentic IDE workflows) as daily practice, not novelty.
Understanding of agentic orchestration patterns — when to use single vs. multi-agent architectures, how to handle memory and state.

Software Engineering

Solid, hands-on software engineering fundamentals: you have built and shipped real systems, not just scripts.
Proficiency in at least one backend language (Python strongly preferred) and the ability to read and contribute across a polyglot codebase.
Experience with REST and streaming APIs, authentication patterns, and integration with enterprise systems.

Data Engineering & Platforms

Ability to build and maintain data pipelines — ingestion, transformation, serving, and monitoring — using tools such as Airflow, Kafka, dbt, or equivalents.
Working familiarity with modern data platforms (Snowflake, Databricks, BigQuery, or similar), including data modeling and performance fundamentals.
Strong debugging instincts with imperfect, real-world data in production environments.

Supply Chain Domain (Nice to Have)

Prior exposure to supply chain planning, logistics, or operations domains is a genuine plus — not a requirement.
Experience with or curiosity about platforms like Kinaxis, SAP IBP, o9, or Blue Yonder is welcome context.

EXPERIENCE & BACKGROUND

3 – 6 years of professional experience in data engineering, data platform engineering, software engineering, or a technical-founder role. Strong candidates at either end of that range are welcome to apply.
Prior exposure to customer-facing technical roles — solutions engineering, forward deployment, technical consulting, or a startup where you wore multiple hats — is a strong signal, though not a requirement.

HOW YOU WILL SUCCEED IN THIS ROLE

You measure your own success in business outcomes — efficiency gains, exception reduction, SLA improvements — not lines of code shipped.
You bring field learnings back into the platform, helping abstract what works into durable, scalable capabilities.
You operate with high agency: you unblock yourself, adapt when conditions change, and take ownership end-to-end without requiring close supervision.
You are curious and pragmatic — you explore new tools and apply them where they create real value, not because they are new.
You communicate clearly across engineering, product, and business stakeholders — written, verbal, and in the room.

#Fulltime, #Full-time #Associate #LI-RJ1 #LI-Hybrid

Work With Impact: Our platform directly helps companies power the world’s supply chains. We see the results of what we do out in the world every day, when we see store shelves stocked, when medications are available for our loved ones, and so much more.

Work with Fortune 500 Brands: Companies across industries trust us to help them take control of their integrated business planning and digital supply chain. Some of our customers include Lockheed Martin, Unilever, P&G, ExxonMobil, Cisco and more.

Social Responsibility at Kinaxis: Our Diversity, Equity, and Inclusion Committee weighs in on hiring practices, talent assessment training materials, and mandatory training on unconscious bias and inclusion fundamentals. Sustainability is key to what we do and we’re committed to a long-term net-zero operations strategy. We are involved in our communities and support causes where we can make the most impact.

People matter at Kinaxis and here are some of the perks and benefits we offer, which may vary by location and employee:

Flexible vacation and Kinaxis Days (company-wide days off)
Flexible work options
Physical and mental well-being programs
Regularly scheduled virtual fitness classes
Mentorship programs, training, and career development
Recognition programs and referral rewards
Hackathons

For more information, visit the Kinaxis website at or the company’s blog at http://blog.kinaxis.com.

Kinaxis welcomes candidates to apply to our inclusive community. We provide accommodations upon request to ensure fairness and accessibility throughout our recruitment process for all candidates, including those with specific needs or disabilities. If you require an accommodation, please reach out to us at [email protected]. This contact information is for accessibility requests only and cannot be used to inquire about the status of applications.

Kinaxis is committed to ensuring a fair and transparent recruitment process. We use artificial intelligence (AI) tools in the initial step of the recruitment process to compare submitted resumes against the job description to identify candidates whose education, experience, and skills most closely match the requirements of the role. After the initial screening, all subsequent decisions regarding your application, including final selection, are made by our human recruitment team. AI does not make any final hiring decisions.

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