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About This Role
Company Description
Afficiency is a rapidly growing Insurtech startup whose mission is to provide life insurance to everyone on the platforms they already trust. Located in NYC, we design life insurance products that can be purchased entirely digitally and can be easily embedded into distribution platforms or with agents who sell insurance. The company is experiencing rapid growth and is well\-funded. We are looking for new team members to join us on our journey to shake up the life insurance industry. We need individuals who bring passion, curiosity, and a desire for excellence.
Job Description
As a Generative AI Engineer at Afficiency, you will be responsible for designing, developing and deploying Generative AI solutions that enhance our core product platforms and client implementations. You will work closely with engineering, data science, and infrastructure teams to build scalable AI\-driven applications using Large Language Models (LLMs), Retrieval\-Augmented Generation (RAG), model fine\-tuning, and reinforcement learning approaches.
This role is ideal for someone who is based in the NYC Metro Area, passionate about building real\-world GenAI applications and bringing them into production, while continuously improving performance, reliability, and user outcomes.
Qualifications Responsibilities
- Deliver GenAI solutions end\-to\-end
- Own technical design and implementation of GenAI applications from discovery through production handoff.
- Build APIs/services that integrate with enterprise systems and analytics platforms.
- Implement enterprise\-grade RAG
- Design ingestion pipelines for internal content (PDFs, policies, research, dashboards, ticketing, wikis).
- Build retrieval systems with hybrid search, filtering, re\-ranking, query rewriting, and context optimization.
- Implement permission\-aware retrieval aligned to entitlements and data access policies.
- Establish evaluation and quality controls.
- Define metrics for retrieval quality and answer grounding (faithfulness, citation accuracy, coverage).
- Create golden datasets, regression tests, and automated evaluation harnesses.
- Operationalize GenAI (LLMOps)
- Instrument observability (latency, cost, token usage, error rates) and implement safe rollout patterns.
- Implement caching, rate limiting, fallbacks, and incident\-ready operational practices.
- Partner across teams to land solutions
- Collaborate with business owners to translate requirements into workable designs.
- Work with Security/Compliance to embed guardrails, auditability, and privacy controls.
- Provide clear documentation and implementation of playbooks to enable internal teams' post\-engagement.
Must Have
- Education: Master's degree or equivalent experience required
- 3\+ years in software engineering, data engineering, ML engineering, or applied AI, including recent GenAI delivery in production.
- Demonstrated expertise in RAG system design and optimization, including:
- chunking \+ metadata enrichment, hybrid search, re\-ranking, retrieval evaluation
- grounding/citations and hallucination mitigation patterns
- Strong Python and backend engineering skills (FastAPI/Flask), plus strong SQL.
- Experience working in regulated or security\-conscious environments, with knowledge of:
- access controls/entitlements, data privacy, logging/audit trails, secure SDLC practices
- Proven ability to work effectively as an IC consultant:
- communicate architecture decisions clearly
- influence cross\-functional stakeholders without direct authority produce high\-quality documentation and handoff materials
Nice to Have
- Fine\-tuning experience (SFT, LoRA/QLoRA) and familiarity with preference optimization concepts (DPO/RLHF)
- Vector/hybrid search platforms: Elasticsearch/OpenSearch vector, FAISS, Pinecone, Weaviate, Milvus
- LLMOps tooling: MLflow/W\&B, OpenTelemetry, prompt registries, evaluation frameworks
- Cloud \+ platform: AWS/Azure/GCP, Docker/Kubernetes, Terraform
Tools \& Technologies
- LLM frameworks: LangChain, LlamaIndex, Semantic Kernel (optional)
- Vector/hybrid search: Open to different skillsets
- Data: (Snowflake/Databricks/warehouse), event pipelines, document stores
- Observability: logging/tracing/metrics, dashboards, alerting
Additional Information What We Offer
- Competitive salary with equity options
- Robust health, dental, and vision benefits for employee and dependents
- 401k matching contributions
- Generous PTO policy
- Provided work\-from\-home equipment
Afficiency is an Equal Opportunity Employer. All your information will be kept confidential according to EEO guidelines.
Salary Context
This $100K-$120K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Afficiency, 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. This role's midpoint ($110K) sits 50% below the category median. Disclosed range: $100K to $120K.
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.
Afficiency AI Hiring
Afficiency has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $120K - $120K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 tracked positions.
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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