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About This Role
AI Development Solution Engineering Specialist
Job Description
Our mission is to develop a firmwide Artificial Intelligence (AI) Development Platform that aligns with the firm's Technology principles and drives efficiency and consistency, controls, security and strong governance and promotes innovation, enabling teams to build applications that leverage AI capabilities and accelerate the adoption of AI across our businesses.
This role is for a solution engineering specialist to work with GenAI application owners to adopt GenAI platform capabilities and develop patterns and best practices to enable GenAI application adoption at scale. The ideal candidate will have strong knowledge and experience on deep learning, contemporary model architectures and a strong drive to solve real-world problems with AI techniques. They will have hands-on experience developing GenAI applications and can leverage their expertise to enable firmwide developers to adopt AI techniques at scale through documentation, consultation, and collaborative Proof of Concept and sample code development.
In the Technology division, we leverage innovation to build the connections and capabilities that power our Firm, enabling our clients and colleagues to redefine markets and shape the future of our communities.
Since 1935, Morgan Stanley is known as a global leader in financial services, always evolving and innovating to better serve our clients and our communities in more than 40 countries around the world.
What you'll do in the role:
> Build Proof of Concepts and example code through which developers can leverage GenAI models to solve common problems or patterns to enable GenAI adoption at scale
> Develop prototypes to demonstrate model capabilities, advanced usage patterns.
> Collaborate with AI engineers to prioritize capabilities for firmwide platform roadmap
> Author best practices on the Generative AI ecosystem, when to use which tools, available models such as GPT, Llama, Hugging Face etc. and libraries such as Langchain, and Sematic Kernel.
> Troubleshoot customer adoption of genAI platform capabilities; update user documentation and provide feedback to platform engineering teams to improve future customer experience
> This is a customer-facing role and excellent soft communications skills are mandatory.
> Have an application developer mindset and build common, reusable solutions to scale Generative AI use cases using pre-trained models as well as fine-tuned models. What you'll bring to the role:
> Bachelor's or a master's degree in computer science or related field, or equivalent job experience
> 8 - 12 years of experience in software engineering, design, and development
> Experience developing GenAI applications
> Strong hands-on proficiency in Python and machine learning specific libraries like numpy, opencv, scikit-learn, pandas, matplotlib, seaborn, etc.
> Understanding of AI/ML Lifecycle and basic understanding of systems like mlflow, Dataiku, databricks.
> Broad understanding of data engineering (SQL, NoSQL, Big Data), data governance, data privacy and security
> Demonstrated experience in DevOps, understanding of CI/ CD. Hands on experience with managing code in code repositories such as Bit Bucket and GitHub.
> Understanding of how deep learning works, understanding of Machine Learning frameworks such as TensorFlow or PyTorch
> Ability to articulate technical concepts effectively to diverse audiences. Excellent communication skills.
> Strong desire and ability to influence development teams and help them adopt AI.
> Demonstrated ability to work effectively and collaboratively in a global organization, across time zones, and across organizations
Nice to have
> Experience working with application development teams to enable them to build AI based applications.
> Ability to create code samples to be shared with application teams.
> Experience working with cloud providers, preferably Azure, but also AWS, GCP
> Experience building applications using AI development services on prominent cloud platforms such as Azure Open AI, Azure AI Foundry, Azure Search, Azure Cognitive Services, AWS Bedrock, AWS Sagemaker, Google Vertex AI
> Experience working in a large, regulated industry such as Financial Services, Insurance etc.
> Experience building AI applications, preferably Generative AI and LLM based apps.
> Experience working with Generative AI development, embeddings, fine tuning of Generative AI models.
> Understanding of Model Ops/ ML Ops/ LLM Ops
What you can expect from Morgan Stanley:
We have a track record of innovation and passion for unlocking new opportunities, we help our clients raise, manage and allocate capital. We do this by offering a wide range of investment banking, securities, wealth management and asset management services.
All that we do at Morgan Stanley is driven by our five core values: do the right thing, put clients first, lead with exceptional ideas, commit to diversity and inclusion, and give back. These aren't just beliefs, they guide the decisions we make every day, ensuring we do what's best for our clients, communities and more than 80,000 employees around the world. And at the core of our success are the people who drive it - relentless collaborators and creative thinkers who are fueled by diverse thinking and experiences.
Wherever you are in our 1,200 global offices, you'll have the opportunity to work alongside the best and the brightest in an environment where you are empowered to achieve your full potential. We are proud to support our employees and their families at every point along their work-life journey, offering some of the most attractive and comprehensive employee benefits and perks in the industry.
Morgan Stanley is an equal opportunities employer. We work to provide a supportive and inclusive environment where all individuals can maximize their full potential. WHAT YOU CAN EXPECT FROM MORGAN STANLEY:
At Morgan Stanley, we raise, manage and allocate capital for our clients – helping them reach their goals. We do it in a way that’s differentiated – and we’ve done that for 90 years. Our values - putting clients first, doing the right thing, leading with exceptional ideas, committing to diversity and inclusion, and giving back - aren’t just beliefs, they guide the decisions we make every day to do what's best for our clients, communities and more than 80,000 employees in 1,200 offices across 42 countries. At Morgan Stanley, you’ll find an opportunity to work alongside the best and the brightest, in an environment where you are supported and empowered. Our teams are relentless collaborators and creative thinkers, fueled by their diverse backgrounds and experiences. We are proud to support our employees and their families at every point along their work-life journey, offering some of the most attractive and comprehensive employee benefits and perks in the industry. There’s also ample opportunity to move about the business for those who show passion and grit in their work.
To learn more about our offices across the globe, please copy and paste https://www.morganstanley.com/about-us/global-offices into your browser.
Expected base pay rates for the role will be between $70,000 and $120,000 per year at the commencement of employment. However, base pay if hired will be determined on an individualized basis and is only part of the total compensation package, which, depending on the position, may also include commission earnings, incentive compensation, discretionary bonuses, other short and long-term incentive packages, and other Morgan Stanley sponsored benefit programs.
Morgan Stanley is an equal opportunity employer committed to building and maintaining a workforce that is diverse in experience and background. Our recruiting efforts reflect our strong commitment to a culture of inclusion, where individuals are hired, developed, and advanced based on their skills and talents.
Our workforce reflects a broad cross-section of the global communities in which we operate, bringing a variety of backgrounds, talents, perspectives, and experiences.
For more information, please visit : https://www.morganstanley.com/people-opportunities/eeo .
Salary Context
This $70K-$120K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Morgan Stanley, 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 $212,040 based on 13,780 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $112,320. This role's midpoint ($95K) sits 55% below the category median. Disclosed range: $70K to $120K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Morgan Stanley AI Hiring
Morgan Stanley has 15 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, Data Scientist. Positions span Bengaluru, Karnataka, India, Alpharetta, GA, US, New York, NY, US. Compensation range: $120K - $195K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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