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About This Role
July 7, 2026
Role: Business Analyst – AI Projects \& Client Success
Employment Type: Full\-time
Location: Hyderabad (Work From Office)
Work Experience: 2–4 years
About the Role
We are looking for a proactive and customer\-focused Business Analyst who can own the complete lifecycle of AI and software development projects—from requirement gathering to successful delivery. The ideal candidate will bridge the gap between clients and technical teams, ensuring project success while identifying opportunities to grow existing client accounts through upselling and cross\-selling.
This role requires a combination of business analysis, AI project understanding, project coordination, client relationship management, and business development skills.
Key Responsibilities
Project Discovery \& Business Analysis
Conduct client meetings to understand business challenges, objectives, and functional requirements.
Prepare Business Requirement Documents (BRDs), Functional Requirement Documents (FRDs), user stories, workflows, wireframes, and acceptance criteria.
Analyze business processes and recommend AI\-driven automation and digital transformation solutions.
Translate business requirements into clear technical specifications for engineering and data science teams.
AI Project Delivery
Coordinate end\-to\-end execution of AI/ML and software development projects.
Work closely with Product Managers, Data Scientists, ML Engineers, Developers, QA, and DevOps teams.
Track project milestones, timelines, dependencies, risks, and deliverables.
Ensure timely project delivery with high quality and customer satisfaction.
Support UAT, production rollout, and post\-deployment activities.
Monitor project progress and proactively resolve delivery bottlenecks.
AI Solution Understanding
Understand AI/ML concepts including Generative AI \& LLMs
Collaborate with technical teams to propose appropriate AI solutions based on client requirements.
Stay updated with emerging AI technologies and industry trends.
Client Relationship Management
Act as the primary point of contact for assigned clients.
Conduct regular project review meetings and provide status updates.
Build long\-term relationships with stakeholders.
Ensure excellent customer experience throughout project execution.
Handle change requests, scope discussions, and expectation management.
Collaboration
Work closely with engineering, pre\-sales, and leadership to align client expectations with delivery reality.
Provide market and client feedback to help shape service offerings and product direction.
Business Development (Existing Accounts)
Identify opportunities for upselling and cross\-selling AI solutions and technology services.
Understand clients’ evolving business needs and propose additional solutions.
Prepare proposals, effort estimations, presentations, and solution documents.
Work closely with leadership during proposal submissions and client presentations.
Support account growth initiatives.
Documentation \& Process Management
Maintain project documentation and knowledge repositories.
Prepare meeting minutes, project plans, requirement documents, and status reports.
Create process flows, use cases, and business process documentation.
Ensure adherence to project management best practices.
Required Skills
Business Analysis
Requirement Gathering
BRD / FRD Preparation
User Stories
Process Mapping
UML / Workflow Diagrams
Gap Analysis
Wireframing (Preferred)
Project Management
Agile \& Scrum Methodologies
Sprint Planning
Risk Management
Stakeholder Management
Project Coordination
Delivery Tracking
AI \& Technology Understanding
Basic understanding of AI/ML lifecycle
Generative AI \& Large Language Models (LLMs)
Prompt Engineering (Preferred)
Client Management
Excellent communication and presentation skills
Client engagement and relationship management
Negotiation and expectation management
Strong problem\-solving and analytical skills
Tools
Jira
Confluence
Microsoft Office
Google Workspace
Qualifications
Bachelor’s degree in Engineering, Computer Science, Information Technology, or a related field.
MBA is an added advantage.
Preferred Experience
3–7 years as a Business Analyst, Project Coordinator, or Client Success Manager.
Experience working with AI/ML, SaaS, or enterprise software projects.
Exposure to Generative AI or data analytics projects is highly desirable.
Experience interacting with international clients is preferred.
Success Metrics
On\-time project delivery
Requirement quality and documentation accuracy
Project delivery success rate
Client retention
Upselling and cross\-selling revenue from existing accounts
Stakeholder satisfaction
Process improvement initiatives
Why Join Us?
Work on cutting\-edge AI and Generative AI projects.
Collaborate with experienced AI engineers and product teams.
Opportunity to work directly with global clients.
Exposure to enterprise AI implementations across multiple industries.
Fast\-paced environment with excellent learning and career growth opportunities.
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At soulpage IT solutions, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
soulpage IT solutions AI Hiring
soulpage IT solutions has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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