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About This Role
Accrete is a dynamic and innovative company focused on transforming the future of artificial intelligence. We specialize in creating advanced AI solutions that turn complex data into actionable insights, driving real\-world impact for businesses and government organizations. Our team thrives on creativity and collaboration, working together to push the boundaries of AI technology.
At the core of our offerings are our AI agents, designed to autonomously analyze data, generate insights, and make intelligent recommendations. These agents help businesses streamline operations, improve decision\-making, and also empower government entities to enhance security, intelligence, and operational efficiency
The Role
Accrete is hiring an AI Platform Engineer on our Machine Learning team. This role focuses on building the systems, infrastructure, and services that power our AI\-driven products.
Depending on the team, you may work on foundational platform infrastructure, backend services, model deployment systems, or the tooling that enables our ML researchers and product teams to move quickly and reliably.
This is an ideal role for engineers with strong Python experience who enjoy building scalable systems and collaborating across product, research, and engineering teams.
What You’ll Do
- Design, build, and maintain scalable backend services and APIs in Python
- Develop platform infrastructure and internal services supporting AI products
- Build and optimize containerized applications using Docker and Kubernetes
- Collaborate with ML, research, and product teams to support the development and deployment of AI models
- Improve system reliability, observability, and performance in production environments
- Contribute to CI/CD pipelines and infrastructure automation
- Participate in code reviews and help establish engineering best practices
What We’re Looking For
- 3\+ years of professional software engineering experience
- Strong programming skills in Python and experience writing production\-quality code
- Experience building backend systems, APIs, or distributed services
- Hands\-on experience with cloud platforms (AWS, GCP, or Azure)
- Familiarity with containerization and infrastructure tooling (Docker, Kubernetes, or similar)
- Strong understanding of software engineering fundamentals including system design and data modeling
- High ownership mindset with the ability to identify problems and drive solutions
- Genuine curiosity about AI, emerging technologies, and building high\-impact systems
Nice to Have
- Experience working with machine learning pipelines or model deployment
- Exposure to MLOps, data engineering, or ML infrastructure
- Familiarity with workflow orchestration tools such as Airflow, Prefect, or Dagster
- Experience collaborating closely with ML or research teams
Core Values \& Expectations:
Impact
You take full ownership and accountability for your work, consistently seeing projects through from inception to completion with a strong bias for action. Proactively identifying challenges, you drive solutions rather than waiting for direction, and hold yourself and others to the highest standards for delivering results. With strategic thinking and a problem\-solving mindset, you make informed decisions leveraging data and expertise, always looking for ways to improve processes, optimize workflows, and enhance outcomes beyond your immediate responsibilities.
Collaboration
You work seamlessly across teams, prioritizing shared goals and team success over individual credit. Engaged listening and open, candid communication are at the heart of your approach, ensuring alignment and synergy throughout the organization. You value diverse perspectives, seeking input from others to drive better results. By treating colleagues with respect and professionalism, you help build a culture of trust, supporting each other through challenges, celebrating successes, and constructively addressing conflicts to strengthen relationships and improve outcomes.
Passion for AI \& Innovation
You are deeply excited about the transformative potential of AI and committed to contributing to a company shaping the future of work. With curiosity and a growth mindset, you continuously seek to learn, adapt, and stay at the forefront of new developments. Your enthusiasm for innovation drives you to explore new ideas, challenge the status quo, and find creative solutions that deliver meaningful impact. You approach your work with energy and a desire to advance both technology and the way we work.
Company Benefits
- Competitive Salary: Aligned with experience and market standards
- Mediclaim: 10 Lacs coverage for you and your family
- Flexible PTO \& Hybrid Work: Take time off when needed and enjoy remote flexibility per company guidelines
- Growth \& Development: Access professional learning opportunities and career advancement support
- Onsite Perks: Enjoy catered lunches, snacks
- Team Bonding: Company\-sponsored social events to connect and unwind
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At accrete, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $178,940 based on 11,900 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
accrete AI Hiring
accrete has 4 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Positions span IN, US, New York, NY, US. Compensation range: $235K - $235K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (119) are outnumbered by mid-level (1,813) and senior (1,472) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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