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About This Role
Role Overview
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The Data/AI Solutions Architect role at Infinite Lambda is designed for a technical leader who bridges sales and engineering, driving growth in a fast\-growing consultancy focused on data and AI solutions. This position is crucial for engaging clients, shaping sales strategies, and expanding into new markets.
The position is titled Data/AI Solutions Architect, reporting directly to the General Manager, US, and is situated within the Global Sales Organization. This leadership role is critical for aligning technical expertise with sales objectives, ensuring Infinite Lambda’s solutions meet client needs effectively.
LOCATION \& WORK ARRANGEMENTS
The role is remote, but you will be expected to travel occasionally to client sites, company meetings, and partner events.
DETAILED RESPONSIBILITIES
PROSPECTIVE CLIENT ENGAGEMENT
- Initiate and manage engagements with prospective clients from the early stages of the sales process, ensuring a strong start to the sales cycle;
- Understand and document client needs, articulating how Infinite Lambda’s products and services can address these needs effectively;
- Work closely with the sales team to foster collaboration and develop compelling proposals that meet
- Conduct and facilitate technical deep\-dive workshops with prospective clients to showcase capabilities and gather insights, using workshop outputs to strengthen proposals;
- Identify relevant Infinite Lambda products and services, working with the sales team to position them strategically for each opportunity;
- Assist both the sales and delivery teams in estimating project scope, effort, and resources, producing realistic and commercially viable account strategies. This includes defining initial team composition, tools to utilise, and a high\-level roadmap.
LEAD SALES ENGINEER STRATEGY
- Work with departments such as Product \& Services, Customer Success, and Sales \& Partnerships to clearly define roles and responsibilities, ensuring seamless collaboration across functions;
- Collaborate with Product \& Services to stay informed about existing offerings and influence product roadmaps, helping design new products and services based on client feedback and market trends;
- Enhance the positioning of Infinite Lambda’s products and services during the sales process and beyond, improving how value is communicated to clients;
- Liaise with senior leadership (CEO, CTO, CPO, COO, CSO) at Infinite Lambda to formulate a clear strategy for combining data and cloud expertise into a holistic value proposition, strengthening the company’s market positioning;
- Conduct regular research on industry trends, working with leadership to evolve strategies as appropriate to maintain competitiveness;
- Deepen subject\-matter knowledge in Infinite Lambda’s key industry verticals, ensuring a strong understanding of client needs and market dynamics.
ADDITIONAL DUTIES
- Work with Infinite Lambda’s executive team to help steer the company’s overall strategy in a positive direction, providing insights from solutions architect perspectives.
- Represent Infinite Lambda at external conferences and events, promoting the company’s brand, thought leadership, and expertise in data and cloud engineering.
REQUIREMENTS
- Education: A Bachelor’s degree in Engineering, Computer Science, or a related field is required; an advanced degree is preferred;
- Experience: A proven track record in data consulting, sales engineering or technical sales, preferably within the data, cloud, or technology sectors;
- Technical expertise: experience in data engineering, analytics engineering and data scientist roles and related technologies, such as AWS, Azure, Snowflake, Redshift, Databricks, dbt and machine learning frameworks is essential;
- Data architecture: Stream processing and batch data warehouse processing, event\-driven architecture, micro services, DataOps and MLOps practices;
- Communication skills: Exceptional verbal and written communication skills, with the ability to translate complex technical concepts into business benefits for clients and stakeholders;
- Leadership and strategy: Experience in leading sales engineering teams or functions, with a strategic mindset to influence company direction and market expansion;
- Collaboration: Strong ability to work effectively across different departments and levels, fostering collaboration with Product \& Services, Customer Success, and Sales \& Partnerships;
- Market expansion: Experience in international markets or expanding into new regions is a plus, particularly knowledge of South American markets and business practices, which is desirable for this role.
- Traveling: The Solutions Architect role Requires travel at least once per month to NYC/California, Philadelphia, etc...
INTERVIEW PROCESS
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- Intro call with TABP: 45\-min
- Interview with General Manager: 45\-min
- Technical interview with Head of Engineering/CTO and Presales Engineer: 90\-min
- Cultural Chat with CEO: 45\-min
Why join Infinite Lambda
- We support your growth with dedicated learning time, access to top\-notch resources and a knowledge\-sharing culture;
- You work autonomously here in a wholesome environment and get as much paid holiday as you need;
- The projects are diverse and give you plenty of opportunity to experiment with new tech;
- The benefits are plenty, with work\-from\-home budget, a company MacBook and more on the list.
Submit your CV in English in PDF.
Mind that as we are an international team, internal communication is in English.
In the US, as a final step in the hiring process, applicants are required to undergo a criminal background check with an accredited third\-party screening company. Employment offers depend on the successful completion of this background check and the verification of all relevant information. We will request your explicit written consent before any screening begins.
We are committed to fair hiring practices and will consider all qualified applicants in accordance with applicable federal, state, and local laws governing criminal background inquiries.
Infinite Lambda is an equal opportunity employer. Our inclusive culture celebrates diversity and treats everyone with dignity and respect. Accordingly, our selection process will never discriminate against applicants on the grounds of any characteristics, such as disability, age, gender, sexual orientation, family status, race, faith or other.
Compensation Range: $180K \- $210K
Salary Context
This $180K-$210K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Infinite Lambda, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($195K) sits 8% above the category median. Disclosed range: $180K to $210K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Infinite Lambda AI Hiring
Infinite Lambda has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Seattle, WA, US. Compensation range: $210K - $210K.
Location Context
AI roles in Seattle pay a median of $227,400 across 1,084 tracked positions. That's 14% above the national 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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