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About This Role
We're looking for a motivated Junior Python AI Developer to help build, deploy, and optimize AI agents in production environments. You'll work closely with senior engineers and customers to bring intelligent automation to life using ADK and Python. This is a hybrid role requiring 3 days onsite in Sunnyvale, CA. If you're passionate about AI, love solving technical problems, and want to grow your career at the forefront of agent\-based systems — we want to hear from you.
Req\# 1060117865
Responsibilities
- Build \& Deploy AI Agents: Design, develop, test, and ship agent workflows using Agent Development Kit (ADK) and Python in production environments
- Develop Integrations: Connect the platform with customer systems, databases, and cloud services using Python and REST APIs
- Test \& Improve Performance: Evaluate agent behavior against real\-world scenarios, investigate failures, and iterate on code/configuration to improve reliability
- Troubleshoot Issues: Debug integration, data, configuration, and agent\-behavior problems across development and production environments
- Collaborate with Customers \& Teams: Participate in technical discussions, document requirements, and translate business needs into clear implementation tasks
- Grow with Emerging AI Tech: Build hands\-on expertise in AI agents, with potential exposure to MCP and RAG pipelines
Requirements
- Experience: Up to 2 years in software engineering, implementation engineering, solutions engineering, or a related technical role (strong internship/project experience considered)
- Python Proficiency: Solid ability to write, debug, test, and maintain production\-quality Python code (OOP, error handling, logging, testing)
- ADK Experience: Hands\-on experience building AI agents using Agent Development Kit (ADK) is required
- Integration Skills: Working knowledge of REST APIs, JSON, authentication methods, and third\-party system integrations
- LLM Fundamentals: Understanding of prompts, context management, tool usage, model limitations, and response evaluation
- Communication \& Ownership: Clear technical communication skills, proactive task management, and attention to documentation and code quality
Nice to have
- Experience with Model Context Protocol (MCP) or Retrieval\-Augmented Generation (RAG) pipelines, including vector databases and embeddings
- Exposure to cloud platforms (GCP, AWS, or Azure), Docker, or CI/CD pipelines
- Previous experience in a customer\-facing engineering, consulting, or support role
We offer
- Health benefits: High Deductible Health Plan with an attached HSA (Health Savings Account) which includes Pharmacy coverage, after 60 days from start of employment
- Condition Management resources
- Family Planning resources
- Dental Plan
- Vision Plan
EPAM is a leading global provider of digital platform engineering and development services. We are committed to having a positive impact on our clients, our employees, and our communities. We embrace a dynamic and inclusive culture. Here you will collaborate with multi\-national teams, contribute to a myriad of innovative projects that deliver the most creative and cutting\-edge solutions, and have an opportunity to continuously learn and grow. No matter where you are located, you will join a dedicated, creative, and diverse community that will help you discover your fullest potential.
Engineer the Future with a Career at EPAM
This posting includes a good faith range of the salary EPAM would reasonably expect to pay the selected candidate. The range provided reflects base salary only. Individual compensation offers within the range are based on a variety of factors, including, but not limited to: geographic location, experience, credentials, education, training; the demand for the role; and overall business and labor market considerations. Most candidates are hired at a salary within the range disclosed. Salary range: $35 \- $40\. In addition, the details highlighted in this job posting above are a general description of all other expected benefits and compensation for the position.
Applications will be accepted on a rolling basis.
In accordance with the LA County Fair Chance Ordinance, you may find a copy of the Notice containing a summary of the Ordinance’s key provisions here: Concept FCO Posting 8 27 24 (lacounty.gov)
Applicants and Employees in Unincorporated Los Angeles County: The Los Angeles Fair Chance Ordinance (LAFCO) requires employers conducting criminal history checks for applicants or employees to state the job duties for which certain criminal history may have a direct, adverse and negative relationship. For this position, these job duties include: engaging with clients, employees, and other third\-parties (including the general public); accessing company and client information, IT systems and infrastructure, assets, property, and products, including proprietary coding, programs, applications, and data; and appropriately handling such information, including company and client confidential and personal information. For this reason, certain criminal history may result in the withdrawal of a condition offer of employment. Qualified applicants with arrest or conviction records will be considered for employment in accordance with the LAFCO and the California Fair Chance Act.
EPAM welcomes all applicants and will consider qualified candidates with criminal history such as arrest and conviction records in a manner consistent applicable law, including the San Francisco Fair Chance Ordinance and Los Angeles Fair Chance Initiative for Hiring.
EPAM will not provide new H\-1B visa sponsorship for this position. Candidates with existing transferable H\-1B status may be considered.
Salary Context
This $72K-$83K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At EPAM Systems, 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 $214,900 based on 6,420 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $110,000. This role's midpoint ($78K) sits 64% below the category median. Disclosed range: $72K to $83K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
EPAM Systems AI Hiring
EPAM Systems has 4 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Remote, US, Sunnyvale, CA, US. Compensation range: $83K - $250K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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