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Description
Job Description – AI Operations Architect \& AWS Engineer, Life Sciences
About Norstella
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Norstella is a premier and critical global life sciences data and AI solutions platform dedicated to improving patient access to life\-saving therapies. Norstella supports pharmaceutical and biotech companies across the full drug development lifecycle — from pipeline to patient. Our mission is simple: to help our clients bring therapies to market faster and more efficiently, ultimately impacting patient lives.
Norstella unites five market\-leading brands (Citeline, Evaluate, MMIT, Panalgo, and The Dedham Group) that all have a shared vision of improving patient access to life\-saving therapy. Norstella delivers must\-have answers and insights for critical strategic, clinical, and commercial decision\-making. We help our clients:
- Accelerate the drug development cycle
- Make data driven commercial and financial decisions
- Match and recruit patients for clinical trials
- Assess competition and bring the right drugs to market
- Identify and address barriers to patient access
- Leverage the latest AI to turn data into insights for faster and better decision making
Norstella serves almost every pharmaceutical and biotech company in the world, along with regulators like the FDA, and payers. By providing critical proprietary data supporting AI\-driven workflows, Norstella helps clients make decisions faster and with greater confidence. Norstella’s investments in AI are transforming how data is consumed and decisions are made, disrupting inefficient legacy workflows and helping the industry become more efficient, innovative, and responsive to patient needs.
About The Position:
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We are seeking an LLM Operations Architect \& AWS Engineer to design, deploy, and operate production\-grade LLM systems on AWS infrastructure at Norstella. You will build and maintain the agentic AI pipelines and cloud architectures that power large\-scale clinical data extraction from real\-world clinical notes, directly accelerating RWD\-driven drug development and treatment access for patients with unmet need.
This role sits at the intersection of ML infrastructure, cloud engineering, and applied NLP — you will own the operational backbone that turns LLM capabilities into reliable, scalable data products.
Key Responsibilities:
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- Design and maintain AWS cloud architectures optimized for LLM inference and fine\-tuning workloads, including GPU instance management, auto\-scaling, and cost optimization across spot and on\-demand capacity.
- Build and operate agentic AI pipelines — multi\-step LLM orchestration workflows with tool use, structured output extraction, retrieval\-augmented generation (RAG), and automated quality control loops.
- Deploy and manage open\-weight LLMs (Llama, Gemma, Qwen, and successors) in production, including model serving infrastructure, batching strategies, and latency/throughput optimization.
- Implement robust evaluation and monitoring frameworks for LLM\-based extraction systems: automated accuracy measurement, drift detection, prompt regression testing, and human\-in\-the\-loop QC integration.
- Develop and maintain CI/CD pipelines for prompt versioning, model updates, and configuration management across development, staging, and production environments.
- Collaborate with NLP scientists, data engineers, and software developers to translate prototype extraction logic into production\-hardened, recurrent processing feeds.
- Ensure compliance with data security requirements (HIPAA, PHI handling) in all LLM deployment and data processing workflows, working closely with IT security and compliance teams.
- Analyze and optimize computational resource utilization — GPU hours, storage, network throughput — balancing cost efficiency against processing SLAs.
- Evaluate and integrate emerging LLM serving technologies, orchestration frameworks, and inference optimization techniques (quantization, speculative decoding, structured generation) to maintain operational edge.
Required Qualifications
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- Master's in Computer Science, Data Science, or related field.
- 5\+ years in production NLP/ML systems development and operations on AWS.
- Hands\-on experience deploying and serving open\-weight LLMs (Llama, Gemma, Qwen families) at scale, using frameworks such as vLLM, TGI, or equivalent serving infrastructure.
- Expert Python proficiency and working knowledge of LLM orchestration tooling (e.g., LangChain/LangGraph, custom agent frameworks).
- Experience with AWS ML/compute services (SageMaker, EC2 GPU instances, S3, Step Functions, Lambda) and infrastructure\-as\-code (Terraform, CloudFormation, or CDK).
- Proven track record deploying large\-scale NLP/LLM solutions in healthcare or life sciences.
- Strong analytical and communication skills; comfortable operating across multiple concurrent projects.
Preferred Qualifications
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- AWS certifications (Solutions Architect, Machine Learning Specialty, or DevOps Engineer).
- Direct experience with PHI handling and HIPAA\-compliant ML infrastructure.
- Experience with AWS Redshift, OpenSearch/Elasticsearch, or similar large\-scale data stores.
- Familiarity with ML experiment tracking and lifecycle management (MLflow, Weights \& Biases, or equivalent).
- Experience building or operating RAG systems, structured extraction pipelines, or clinical NLP applications.
Our guiding principles for success at Norstella:
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- Bold, Passionate, Mission\-First We have a lofty mission to Smooth Access to Life Saving Therapies, and we will get there by being bold and passionate about the mission and our clients. Our clients and the mission in what we are trying to accomplish must be in the forefront of our minds in everything we do.
- Integrity, Truth, Reality We make promises that we can keep, and goals that push us to new heights. Our integrity offers us the opportunity to learn and improve by being honest about what works and what doesn’t. By being true to the data and producing realistic metrics, we are able to create plans and resources to achieve our goals.
- Kindness, Empathy, Grace We will empathize with everyone’s situation, provide positive and constructive feedback with kindness, and accept opportunities for improvement with grace and gratitude. We use this principle across the organization to collaborate and build lines of open communication.
- Resilience, Mettle, Perseverance We will persevere – even in difficult and challenging situations. Our ability to recover from missteps and failures in a positive way will help us to be successful in our mission.
- Humility, Gratitude, Learning We will be true learners by showing humility and gratitude in our work. We recognize that the smartest person in the room is the one who is always listening, learning, and willing to shift their thinking.
Norstella is an equal opportunities employer and does not discriminate on the grounds of gender, sexual orientation, marital or civil partner status, pregnancy or maternity, gender reassignment, race, color, nationality, ethnic or national origin, religion or belief, disability or age. Our ethos is to respect and value people’s differences, to help everyone achieve more at work as well as in their personal lives so that they feel proud of the part they play in our success. We believe that all decisions about people at work should be based on the individual’s abilities, skills, performance and behavior and our business requirements. Norstella operates a zero\-tolerance policy to any form of discrimination, abuse or harassment.
All legitimate roles with Norstella will be posted on Norstella’s job board which is located at norstella.com/careers. If a role is not posted on this job board, a candidate should assume the role is not a legitimate role with Norstella. Norstella is not responsible for an application that may be submitted by or through a third\-party and candidates should proceed with extreme caution if a third\-party approaches them about an open role with Norstella. Norstella will never ask for anything of value or any type of payment during or as part of any recruitment, interview, or pre\-hire onboarding process. If you are aware of or have reason to believe a job posting purportedly for a role with Norstella is fraudulent or otherwise not authorized by Norstella, please contact the Company using the following email address: [email protected]
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 Norstella, 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. Mid-level AI roles across all categories have a median of $194,400.
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.
Norstella AI Hiring
Norstella has 3 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% 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 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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