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About This Role
Job Description
Business Analyst GenAI SDLC Transformation
As a Business Analyst, you will lead discovery and requirements analysis for a Generative AI-enabled SDLC transformation program. You will translate business needs, engineering pain points, and strategic objectives into clear processes, prioritized use cases, user stories, acceptance criteria, and measurable outcomes.
The Role
You will serve as the bridge among product, engineering, architecture, data, security, risk, and change-management teams from initial concept through pilot, delivery, validation, and adoption. This is primarily a business analysis role; success depends on structured analysis, stakeholder alignment, and strong product thinking rather than hands-on model development.
Key Responsibilities
Business Analysis and Discovery
- Lead stakeholder interviews, workshops, process reviews, and value-stream mapping to understand current-state SDLC workflows and define the target state.
- Elicit, analyze, validate, and document business, functional, nonfunctional, data, integration, and control requirements.
- Identify pain points and automation opportunities across planning, requirements, development, testing, release, documentation, and operations.
- Create process maps, user journeys, use cases, user stories, acceptance criteria, decision logs, and requirements traceability artifacts.
- Challenge assumptions, clarify scope, resolve requirement gaps, and maintain alignment between business objectives and solution capabilities.
Product Backlog and Delivery
- Partner with product owners and program leaders to define the product vision, roadmap, minimum viable product, and phased release plan.
- Refine and prioritize the backlog using business value, user impact, feasibility, risk, dependencies, and readiness criteria.
- Facilitate backlog refinement, sprint planning, demonstrations, and other delivery ceremonies while providing timely requirement clarification.
- Coordinate user acceptance testing, including test scenarios, participant readiness, defect triage, signoff, and validation against expected outcomes.
- Track requirement changes, decisions, risks, assumptions, dependencies, and open issues throughout the delivery lifecycle.
GenAI Use Cases and Responsible Adoption
- Define personas, user journeys, and use cases for AI assistants, copilots, and agentic workflows across the software delivery lifecycle.
- Work with technical teams to define required context, data sources, integrations, human review points, exception paths, and operational controls.
- Help establish evaluation criteria for output quality, groundedness, safety, usability, reliability, and business value before broader rollout.
- Document privacy, security, responsible AI, model risk, auditability, and regulatory requirements and support the appropriate review processes.
- Support pilots, feedback sessions, training, communications, and adoption activities that enable teams to use new capabilities effectively.
Value Measurement and Stakeholder Communication
- Establish baselines and define success measures for cycle time, productivity, quality, defects, adoption, user satisfaction, and risk reduction.
- Analyze qualitative and quantitative feedback, identify trends, and recommend backlog or process changes based on evidence.
- Prepare concise status updates, decision materials, process documentation, and presentations for delivery teams and senior stakeholders.
- Maintain a shared understanding of scope, priorities, expected benefits, delivery progress, and unresolved decisions across stakeholder groups.
Required Qualifications
Business Analysis and Product Skills
- Bachelor's degree in Business, Information Systems, Computer Science, Engineering, or a related field, or equivalent professional experience.
- 5+ years of business analysis, product analysis, or product ownership experience supporting enterprise technology transformation.
- Strong capability in requirements elicitation, process modeling, requirements decomposition, user stories, acceptance criteria, and traceability.
- Demonstrated experience facilitating cross-functional workshops and aligning stakeholders with different priorities and levels of technical knowledge.
- Working knowledge of Agile delivery methods and the end-to-end Software Development Life Cycle.
- Experience with collaboration, backlog, and process-mapping tools such as Jira, Confluence, Azure DevOps, Visio, Lucidchart, or similar platforms.
GenAI and SDLC Knowledge
- Working understanding of Generative AI concepts, including large language models, prompt design, retrieval-augmented generation, AI agents, and AI coding assistants.
- Ability to translate technical concepts, constraints, and risks into clear business language, requirements, and decision options.
- Experience identifying or assessing automation and AI use cases within software engineering, technology operations, or enterprise workflows.
- Understanding of responsible AI, data privacy, security, human oversight, output evaluation, and change-management considerations.
- Ability to engage effectively with engineers and architects without requiring hands-on software development or model-building responsibilities.
Delivery Governance and Communication
- Strong analytical, problem-solving, facilitation, written communication, and presentation skills.
- Experience supporting testing, release readiness, implementation, training, adoption, and post-launch feedback cycles.
- Ability to manage ambiguity, organize complex information, and balance multiple priorities in a fast-moving transformation program.
- Proven ability to build trust with business and technology stakeholders and drive decisions to closure.
- Commitment to maintaining clear documentation, transparent decision-making, and disciplined requirements governance.
Preferred Qualifications
- Experience supporting enterprise SDLC modernization, developer productivity, DevOps, platform engineering, or technology operating-model initiatives.
- Familiarity with enterprise AI assistants and platforms such as GitHub Copilot, Microsoft Copilot, Azure OpenAI, or comparable tools.
- Business analysis, product, Agile, or change-management certification such as CBAP, CCBA, CSPO, PSPO, SAFe, or Prosci.
- Experience developing business cases, evaluating vendors, defining value metrics, or supporting technology investment decisions.
What Success Looks Like
- Experience supporting enterprise SDLC modernization, developer productivity, DevOps, platform engineering, or technology operating-model initiatives.
- Create a validated, prioritized backlog that links stakeholder needs and SDLC pain points to clear GenAI-enabled outcomes.
- Reduce delivery ambiguity and rework through complete, testable, traceable requirements and timely decisions.
- Help pilots meet defined standards for business value, usability, output quality, security, and responsible use.
- Enable sustained adoption through effective stakeholder engagement, feedback loops, communications, training, and change support.
- Demonstrate measurable improvements in delivery speed, quality, productivity, and user experience without compromising enterprise controls.
Why Join Merwel
- Work on high-impact Generative AI and enterprise transformation initiatives.
- Help shape how AI improves software delivery and developer experience in complex enterprise environments.
- Collaborate with experienced product, engineering, AI, data, risk, and transformation leaders.
- Influence the responsible adoption of AI-enabled ways of working across technology teams.
- Build expertise in one of the fastest-evolving areas of business and technology transformation.
Pay: $98,187.95 - $118,247.86 per year
Work Location: Hybrid remote in Phoenix, AZ 85012
Salary Context
This $98K-$118K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Merwel, 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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($108K) sits 49% below the category median. Disclosed range: $98K to $118K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Merwel AI Hiring
Merwel has 2 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer. Positions span US, Phoenix, AZ, US. Compensation range: $118K - $132K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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