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About This Role
Job Description
Job Description
We are seeking a highly skilled GenAI / Agentic AI Engineer to design, build, and deploy autonomous, LLM\-powered systems that solve complex business problems at scale. This role focuses on agentic workflows, retrieval\-augmented generation (RAG), tool orchestration, evaluation, and production deployment of GenAI systems.
You will work at the intersection of LLMs, systems engineering, and applied ML, building intelligent agents that reason, plan, interact with tools, and operate reliably in real\-world environments\-particularly across regulated domains such as healthcare.
Job Duties
Agentic AI \& GenAI System Development:
- Design, build, and deploy agentic AI systems using LLMs, tools, memory, and planning frameworks.
- Implement multi\-agent and single\-agent workflows for autonomous task execution, decision support, and orchestration.
Develop tool\-using agents (function calling, structured outputs, APIs, databases, workflows).
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Retrieval\-Augmented Generation (RAG):
- Design and optimize RAG pipelines, including document ingestion, chunking strategies, embeddings, vector stores, and retrieval ranking.
- Implement advanced retrieval techniques (hybrid search, metadata filtering, re\-ranking, query rewriting).
Evaluate and tune RAG systems for accuracy, latency, grounding, and hallucination reduction.
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Model Adaptation \& Optimization:
- Fine\-tune and adapt foundation models (instruction tuning, LoRA, adapters) for domain\-specific use cases.
- Optimize prompts, schemas, and system instructions for reliability and determinism.
Apply reinforcement or feedback\-driven optimization where applicable (human or automated eval loops).
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Evaluation, Monitoring \& Governance:
- Define evaluation frameworks for GenAI systems, including task success, factuality, grounding, latency, and cost.
- Build monitoring and observability for agent behavior, tool calls, and failure modes.
Partner with governance and risk teams to ensure responsible AI practices, traceability, and compliance.
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Production Deployment \& MLOps for GenAI:
- Deploy GenAI and agentic systems into production using cloud\-native architectures.
- Implement CI/CD, versioning, rollback, and runtime safeguards for LLM applications.
Optimize systems for performance, cost efficiency, and scalability.
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Collaboration \& Leadership:
- Collaborate closely with software engineers, product managers, data scientists, and business stakeholders.
- Translate ambiguous business problems into well\-structured agentic solutions.
Mentor junior engineers and contribute to GenAI best practices and internal standards.
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Job Qualifications
Technical Skills:
- Strong Python proficiency and experience building production\-grade services.
- Deep understanding of LLMs and foundation models (GPT, Claude, Llama, etc.).
- Hands\-on experience with agent frameworks (e.g., LangGraph, Semantic Kernel, DSPy, AutoGen, CrewAI, custom frameworks).
- Strong knowledge of RAG architectures, vector databases, and embedding models.
- Experience with structured outputs, function calling, JSON schemas, and tool orchestration.
- Familiarity with LLM evaluation techniques and failure mode analysis.
- Experience with APIs, microservices, and distributed systems.
- Problem Solving \& Communication
- Strong analytical thinking and ability to structure ambiguous problems.
- Ability to explain complex GenAI concepts to both technical and non\-technical audiences.
Proven ability to work cross\-functionally in fast\-moving environments.
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REQUIRED EDUCATION:
Master’s Degree in Computer Science, Data Science, Statistics, or a related field
REQUIRED EXPERIENCE/KNOWLEDGE, SKILLS \& ABILITIES:
- 6\+ years’ work experience as a data scientist preferably in healthcare environment but candidates with suitable experience in other industries will be considered
- Knowledge of big data technologies (e.g., Hadoop, Spark)
- Familiar with relational database concepts, and SDLC concepts
- Demonstrate critical thinking and the ability to bring order to unstructured problems
- Technical Proficiency: Strong programming skills in languages such as Python and R, and experience with machine learning frameworks like TensorFlow, Keras, or PyTorch.
- Statistical Analysis: Excellent understanding of statistical methods and machine learning algorithms, including k\-NN, Naive Bayes, SVM, and neural networks.
- Experience with Agentic Workflows: Familiarity with designing and implementing agentic workflows that leverage AI agents for autonomous operations.
- RAG Techniques: Knowledge of retrieval\-augmented generation techniques and their application in enhancing AI model outputs.
- Model Fine\-Tuning Expertise: Proven experience in fine\-tuning models for specific tasks, ensuring they meet the required performance metrics.
- Data Visualization: Proficiency in data visualization tools (e.g., Tableau, Power BI) to present complex data insights effectively.
- Database Management: Experience with SQL and NoSQL databases, data warehousing, and ETL processes.
- Problem\-Solving Skills: Strong analytical and problem\-solving abilities, with a focus on developing innovative solutions to complex challenges.
PREFERRED EDUCATION:
PHD or additional experience
PREFERRED EXPERIENCE:
- Experience with cloud platforms (e.g., Databricks, Snowflake, Azure AI Studio etc.) for working with AI workflows and deploying models.
- Familiarity with natural language processing (NLP) and computer vision techniques.
To all current Molina employees: If you are interested in applying for this position, please apply through the intranet job listing.
Molina Healthcare offers a competitive benefits and compensation package. Molina Healthcare is an Equal Opportunity Employer (EOE) M/F/D/V.
Pay Range: $87,568 \- $189,732 / ANNUAL
\*Actual compensation may vary from posting based on geographic location, work experience, education and/or skill level.
Salary Context
This $87K-$189K range is below the median for Data Scientist roles in our dataset (median: $155K across 226 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Molina Healthcare, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 463 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($138K) sits 28% below the category median. Disclosed range: $87K to $189K.
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.
Molina Healthcare AI Hiring
Molina Healthcare has 1 open AI role right now. They're hiring across Data Scientist. Based in US. Compensation range: $189K - $189K.
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 Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
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).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
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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