Senior individual contributor who leads small engineering teams building production AI applications — LLM agents, RAG pipelines, and evaluation frameworks — primarily in Python on AWS (Bedrock), while owning architecture, mentoring engineers, and driving features from design through deployment. Fully remote, US-based role.
Compensation
Not disclosed
Location
US
Work setup
Remote
Seniority
Senior
Employment
Not specified
Experience
7+ years
Category
Ai engineering
Posted
Skills
agentic-ai
ai
api
assembly
aws
aws-bedrock
ci-cd
cloud
distributed-systems
dspy
dynamodb
embeddings
faiss
gdpr
lambda
langchain
langgraph
langsmith
llm
observability
opensearch
opentelemetry
pgvector
pinecone
python
rag
rds
s3
saas
semantic-kernel
semantic-search
sqs
test-automation
vector-databases
weaviate
workflow-orchestration
Must have
U.S. citizenship or authorization to work in the United States
7+ years of professional software engineering experience delivering production systems
Strong proficiency in Python and modern backend software engineering
Technical leadership experience for engineering projects or small teams (architecture, mentorship, delivery)
Hands-on experience building and deploying production AI applications with LLMs, agentic workflows, or retrieval systems
Strong understanding of backend architecture, APIs, distributed systems, databases, and workflow orchestration
Experience leading technical initiatives in startup or rapidly scaling SaaS environments
Track record of improving engineering practices and technical maturity across teams
Ability to balance technical debt, delivery speed, operational reliability, and long-term architecture
Full description
This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Applied AI Engineer based in the United States.
Take a leading role in designing and delivering sophisticated AI-powered applications that solve complex business challenges and create meaningful customer impact. As a senior individual contributor, you will combine hands-on software engineering with technical leadership, guiding small engineering teams through the development of production-grade AI capabilities. You will work across agentic workflows, large language models, retrieval systems, and autonomous business processes to build intelligent, reliable product experiences. Beyond implementation, you will shape architectural decisions, translate ambiguous requirements into actionable plans, and drive initiatives from concept through production. You will also strengthen engineering standards through mentorship, code reviews, and technical guidance. This is an opportunity to influence both the evolution of AI products and the technical maturity of a fast-moving, fully remote organization.
Accountabilities
You will lead high-impact AI engineering initiatives while remaining actively involved in implementation, ensuring that technical solutions are scalable, reliable, maintainable, and aligned with business objectives.
Technical Leadership and Delivery
- Lead small engineering teams in designing, developing, and delivering complex AI initiatives.
- Own technical execution throughout the project lifecycle, from initial architecture and implementation to testing, deployment, and ongoing operations.
- Translate ambiguous product requirements into clear technical strategies, actionable engineering tasks, and realistic delivery milestones.
- Build and optimize retrieval-augmented generation (RAG) systems, including ingestion pipelines, embeddings, semantic search, and context assembly.
- Integrate foundation models through platforms such as Amazon Bedrock and AWS AgentCore.
- Develop AI evaluation frameworks using regression testing, golden datasets, trace analysis, and quality measurement.
- Improve production AI systems across reliability, accuracy, latency, observability, and cost efficiency.
- Design robust systems capable of handling unpredictable model behavior, edge cases, operational failures, and real-world production constraints.
- Build and improve orchestration systems, infrastructure, backend services, APIs, and integrations that enable AI systems to interact safely with production applications.
Engineering Excellence and Mentorship
- Raise engineering standards through code reviews, architectural guidance, hands-on mentorship, and technical collaboration.
- Mentor engineers in software design, debugging, testing strategies, system architecture, and production engineering practices.
- Establish best practices for code quality, automated testing, documentation, maintainability, and operational readiness.
- Help engineers strengthen their technical judgment, ownership, independence, and problem-solving capabilities.
- Identify recurring technical weaknesses, unnecessary complexity, and process inefficiencies, and implement improvements.
- Promote engineering practices that prioritize reliability, clarity, testability, and long-term maintainability.
- Lead by example through high-quality implementation, constructive feedback, effective collaboration, and personal accountability.
- Improve development tooling and workflows to increase engineering productivity, software quality, and operational reliability.
Requirements
The ideal candidate is an experienced software engineer with deep practical knowledge of production AI systems, strong Python and backend development skills, and the ability to lead technical initiatives while remaining hands-on.
Required qualifications
- U.S. citizenship or authorization to work in the United States.
- At least 7 years of professional software engineering experience, including substantial responsibility for designing and delivering production systems.
- Strong proficiency in Python and modern backend software engineering.
- Demonstrated experience providing technical leadership for engineering projects or small teams, including architecture, implementation, mentorship, and delivery.
- Proven hands-on experience designing, building, and deploying production AI applications using LLMs, agentic workflows, retrieval systems, or related technologies.
- Strong understanding of backend architecture, APIs, distributed systems, databases, and workflow orchestration.
- Knowledge of production AI challenges, including model reliability, hallucination mitigation, evaluation, observability, latency, and cost management.
- Commitment to software engineering best practices, including automated testing, code reviews, CI/CD, system design, and maintainability.
- Experience working with cloud infrastructure, preferably AWS.
- Ability to mentor engineers, provide constructive technical feedback, and influence engineering practices.
- Strong technical judgment, debugging capabilities, and the ability to navigate complex systems and ambiguous requirements.
- Ability to balance hands-on engineering contributions with technical leadership responsibilities.
- Excellent communication and collaboration skills, particularly when working across engineering, product, and business teams.
- Adaptability and strong ownership in fast-moving environments where execution and accountability are essential.
Preferred qualifications
- Experience with Amazon Bedrock, AWS AgentCore, Lambda, Step Functions, S3, DynamoDB, RDS, SQS, or related AWS services.
- Familiarity with LangGraph, LangChain, DSPy, Semantic Kernel, or similar agent orchestration frameworks.
- Experience building multi-step autonomous agents that interact with tools, APIs, external systems, or business workflows.
- Experience developing AI evaluation frameworks, prompt regression testing, trace analysis, or human-in-the-loop review systems.
- Familiarity with vector databases and semantic retrieval technologies such as OpenSearch, pgvector, Pinecone, Weaviate, or FAISS.
- Experience with LLM observability and evaluation platforms such as LangSmith, Arize, Helicone, Weights & Biases, or OpenTelemetry.
- Experience leading technical initiatives in startup environments or rapidly scaling SaaS organizations.
- Track record of improving engineering practices, introducing development standards, or strengthening technical maturity across teams.
- Ability to balance technical debt, delivery speed, operational reliability, and long-term architectural decisions.
Personal qualities and success indicators
- Takes ownership and leads through technical expertise, sound judgment, and example.
- Can break down complex technical challenges, establish clear solutions, and guide teams through successful implementation.
- Values engineering craftsmanship while maintaining a pragmatic focus on delivering business value.
- Enjoys mentoring colleagues, challenging assumptions, and helping others become more capable and independent.
- Takes responsibility for both individual contributions and the quality of the work delivered by the teams they support.
- Demonstrates the ability to deliver high-impact initiatives, build reliable and observable AI systems, improve engineering quality, and strengthen the technical capabilities of colleagues.
Benefits
- Fully remote work environment.
- Opportunity to work on advanced AI applications, autonomous agents, and production-scale intelligent systems.
- Significant technical ownership and influence over architecture, engineering practices, and product capabilities.
- Opportunity to lead and mentor engineers while remaining hands-on with implementation.
- Collaborative environment focused on innovation, engineering excellence, and meaningful business outcomes.
- Opportunity to help develop AI solutions supporting nonprofit fundraising and organizations with important social missions.
How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
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