Software Dev Engineer II, SIFT

Every day the people closest to our customers and partners learn things that matter in meetings in deal rooms in the field. For years most of that went nowhere useful: stuck in an inbox a memory a note no one opened again. We changed that. We built a product that captures those raw field inputs automatically finds the patterns hiding across thousands of them and uses AI to turn what used to be hours of manual report-writing into minutes and its now part of how a large fast-growing community does its job every day.

Thats the part that makes this interesting: it already works at real scale for a lot of people and that raises the bar rather than lowering it. The hard problems now are the ones you only get to when something is genuinely in production and genuinely depended on. How do you keep quality high as usage climbs and the data gets messier How do you make AI-generated output people trust instead of double-check How do you extend a system people already rely on without breaking what they count on This is applied AI in the critical path of real work where mostly right isnt good enough and someone notices the moment its off.

Thats where you come in.

Heres the kind of thing youd own and solve:

- Scale whats already working.
Take services that are live and heavily used and make them faster sturdier and ready for the next order of magnitude of users and data.

- Grow what the product can connect to.
Expand the set of tools and integrations the system exposes through the Model Context Protocol (MCP) so the product can plug into more data sources and let AI assistants do more on a users behalf safely and reliably.

- Raise the quality bar on AI output.
Improve how the product turns messy free-form input into trustworthy structured searchable records with smarter summarization tagging and quality gating so strong output ships and weak output doesnt.

- Extend the product thoughtfully.
Add new capabilities and data sources to a system people already depend on without disrupting the workflows they rely on every day.

- Make the trends sharper.
Improve how the product spots patterns across a huge body of individual stories so people see whats emerging without digging for it.

- Let people talk to their data.
Build and refine the natural-language experiences that let someone ask for what they need and get an answer built from real data and only the data theyre allowed to see.

- Keep it healthy.
Own the reliability and operations of what you build dig into production issues when they happen and raise the operational bar for the whole team.

What sets this apart: youre joining something that already succeeded and the interesting work is everything that comes next. You get the best of both worlds: the real users real feedback and real impact of a product in heavy use plus a wide-open set of hard problems around scaling it deepening the AI and taking it places it hasnt gone yet. Youll make architectural calls that matter see your impact quickly because the people using it will tell you exactly whats working and be expected to lift the engineers around you mentoring reviewing designs and being someone who makes the whole team sharper not just your own code.

Were looking for someone whos built and operated real backend systems at scale is comfortable improving a live product without breaking it and is genuinely excited about applying AI to a hard valuable problem rather than chasing the buzz. Hands-on experience with generative AI and large language models is a strong prerequisite and familiarity with Amazon Bedrock in particular will set you up to make an impact fast. If you like owning things end to end caring about the person on the other side of the screen and raising the bar on something people already love youll feel at home here.

Key job responsibilities
Youll own features end to end from the user interface down to the backend services behind them designing building and running them in production where real people rely on them every day.

- Own whole features from the screen a user sees to the services and data underneath making the design calls across the full stack and shipping each one to production.
- Build and scale the services that turn raw free-form field input into structured searchable trustworthy records keeping them fast and reliable as usage grows.
- Expand the tools and integrations the product exposes through the Model Context Protocol (MCP) connecting it to more data sources and letting AI assistants safely do more on a users behalf.
- Wire large language models into the product and raise the quality bar on what they produce sharper summarization tagging and quality gating so strong output ships and weak output doesnt.
- Build the natural-language experiences that let people ask for what they need and get answers grounded in real data and only the data theyre allowed to see.
- Write the design docs run code reviews debug the gnarly production issues and mentor the engineers around you.

A day in the life
Youre working where solid engineering meets applied AI shipping real features people use the moment they go out.

You might push a feature you own from the screen a user sees down to the service behind it: write the code get it reviewed shepherd it through testing into production. A good chunk of your time goes into making the AI better tuning how it drafts and summarizes then checking the output against real examples to prove it improved. Youll join a short daily standup and review teammates code.

Some days stretch further debugging production issues or prototyping an idea.

About the team
We build AI that changes how the sales field works. Our users are the people talking to AWS customers and partners every day and our job is to turn the stories they gather into something they can use right away. We capture their input surface the patterns across it and draft the reports that used to eat hours of someones week. Youll work shoulder to shoulder with engineers product and the field teams who depend on what we ship to help shape where the product goes next. We move fast listen to our users and care about getting it right.

About AWS
Diverse Experiences
AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description we encourage candidates to apply. If your career is just starting hasnt followed a traditional path or includes alternative experiences dont let it stop you from applying.

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Inclusive Team Culture
Here at AWS its in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences inspire us to never stop embracing our uniqueness.

Mentorship & Career Growth
Were continuously raising our performance bar as we strive to become Earths Best Employer. Thats why youll find endless knowledge-sharing mentorship and other career-advancing resources here to help you develop into a better-rounded professional.

Work/Life Balance
We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home theres nothing we cant achieve in the cloud.

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- 3 years of non-internship professional software development experience
- 2 years of non-internship design or architecture (design patterns reliability and scaling) of new and existing systems experience
- 1 years of designing and developing large-scale multi-tiered multi-threaded embedded or distributed software applications tools systems and services using: C# C Java or Perl experience
- 1 years of Object Oriented Design experience
- Bachelors degree or foreign equivalent in Computer Science Engineering Mathematics or a related field
- Knowledge of Python and/or C programming

- 3 years of full software development life cycle including coding standards code reviews source control management build processes testing and operations experience
- Bachelors degree in computer science or equivalent
- Knowledge of Machine Learning and LLM fundamentals including transformer architecture training/inference lifecycles and optimization techniques

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status disability or other legally protected status.

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process including support for the interview or onboarding process please visit for more information. If the country/region youre applying in isnt listed please contact your Recruiting Partner.

The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience qualifications and location. Amazon also offers comprehensive benefits including health insurance (medical dental vision prescription Basic Life & AD&D insurance and option for Supplemental life plans EAP Mental Health Support Medical Advice Line Flexible Spending Accounts Adoption and Surrogacy Reimbursement coverage) 401(k) matching paid time off and parental leave. Learn more about our benefits at MA Boston - 143700.00 - 194400.00 USD annually