AI Engineer

Tegna Inc.
United States
about 2 months ago

Role details

Contract type
Temporary contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
5 years minimum
Working hours
Regular working hours
Job source

Tech stack

Microsoft Access XML Schema Application Programming Interfaces (APIs) Artificial Intelligence Amazon Web Services Amazon Elastic Compute Cloud Business Analytics Applications Application Performance Management Batch Processing Cloud Computing Cloud Engineering Information Engineering
+24 more
Data Systems JSON Python (Programming Language) Software Deployment Systems Integration Management of Software Versions Enterprise Data Management Data Logging Large Language Models Snowflake Grafana Prompt Engineering Model Validation Generative AI Backend AI Platforms Kubernetes Machine Learning Operations Virtual Agents Cloudwatch Software Version Control Data Pipelines Serverless Computing Microservices

Job description

AI & Generative AI Development

  • Design, develop, and deploy LLM-powered applications and agentic AI systems in production environments.
  • Implement advanced prompt engineering strategies including:
  • Prompt chaining and multi-turn orchestration
  • Few-shot learning and in-context learning
  • Chain-of-Thought (CoT) and Tree-of-Thought (ToT) prompting
  • Function calling and tool use optimization
  • Structured output generation (JSON, XML schemas)
  • Build and optimize Retrieval-Augmented Generation (RAG) systems integrating Snowflake data with LLMs.
  • Evaluate and fine-tune foundation models via AWS Bedrock or other managed AI services.
  • Develop guardrails for AI systems including hallucination mitigation, grounding, and safety controls.
  • Implement LLMOps best practices for model lifecycle management:
  • Model versioning, deployment, and rollback strategies
  • Prompt versioning and experimentation frameworks
  • Monitor and observe LLM application performance using observability tools.
  • Evaluation frameworks for LLM outputs

Cloud & Platform Engineering (AWS)

Architect scalable AI solutions using AWS services such as:

  • Bedrock - Sagemaker - Access and fine-tune foundation models
  • Lambda - Serverless LLM application deployment
  • EC2 - GPU-accelerated inference and batch processing
  • Step Functions - Orchestrate complex LLM workflows and agentic pipelines
  • CloudWatch - Monitoring, logging, and alerting for AI systems

AI Application Development

  • Build APIs and backend services to operationalize AI solutions.
  • Integrate LLM/AI systems into internal applications, sales tools, or analytics platforms.
  • Implement streaming and real-time inference for low-latency AI applications.
  • Collaborate with stakeholders to translate use cases into production AI systems., Recruiters or Hiring Managers will never request payments, ask for financial account information or sensitive information such as social security numbers.

Requirements

The ideal candidate combines strong cloud engineering expertise with hands-on experience in prompt engineering, foundation models, agentic AI systems, and data pipelines within Snowflake and AWS ecosystems, * 5+ years of experience in AI/ML, Software or Data engineering.

  • Proficiency in Python with solid understanding of ML fundamentals
  • Strong hands-on experience with AWS, APIs and microservices architecture
  • Experience integrating AI solutions with data systems like Snowflake.
  • Practical experience with prompt engineering
  • Experience with LLM orchestration frameworks (e.g., LangChain, LlamaIndex, Semantic Kernel, or similar
  • Experience with agentic frameworks (AutoGen, CrewAI, or equivalent).

Preferred Qualifications

  • Experience building RAG pipelines in enterprise environments.
  • Knowledge of MLOps best practices.
  • Experience with vector databases and embeddings.
  • Familiarity with model evaluation frameworks (e.g., LLM eval metrics).
  • Experience implementing AI governance and responsible AI practices.
  • Background in sales, media, marketing analytics, or enterprise data platforms (a plus). #LI-MS1

Benefits & conditions

TEGNA offers comprehensive benefits designed to safeguard the physical, mental and financial health of our employees and their families. TEGNA offers two medical plan options for full and part-time employees through Blue Cross Blue Shield of Texas, as well as access to dental and eye care coverage; fertility, surrogacy and adoption assistance; disability and life insurance.

Our 401(k) program offers full, part-time and temporary employees the opportunity to contribute 1% - 80% of their pay on a pre-tax basis to TEGNA’s 401(k). Contributions made up to the first 4% of pay are eligible for a 100% match from the company and are 100% vested from day one.

Regardless of participation in TEGNA medical plans, ALL employees and their eligible family members receive nine free virtual doctor’s appointments with a physician through Teladoc, and 12 free annual therapy sessions with a licensed clinician through Spring Health.

TEGNA offers a generous Paid Time Off (PTO) benefit as well as nine paid holidays per year.

About the company

TEGNA Inc. (NYSE: TGNA) helps people thrive in their local communities by providing the trusted local news and services that matter most. With 64 television stations in 51 U.S. markets, TEGNA reaches more than 100 million people monthly across the web, mobile apps, streaming, and linear television. Together, we are building a sustainable future for local news.

We are seeking an AI Engineer to design, develop, and deploy scalable LLM-powered solutions leveraging AWS cloud services, Snowflake, and modern GenAI frameworks. This role focuses on building production-grade AI systems, optimizing LLM inference, and integrating enterprise data platforms with cutting-edge AI technologies.

Apply for this position

This job is hosted externally. Click below to view the full posting and apply.

Apply on dice.com

Good distractions

Talks and stories from around this role — technically off-topic, practically not.

3:03 min

Career evolution in data engineering and AI platforms

Maria Apazoglou · Coffee With Developers

3:47 min

Exploring JSON, CBOR, and JOSE for data serialization

Aaron Russell · LIVE

1:52 min

Structuring and scaling the backend engineering team

Stefan Lingler Stefan Lingler +1 · Coffee With Developers

10:40 min

Visualizing Prometheus open metrics using custom Grafana dashboards

Stijn Polfliet · LIVE

2:08 min

Essential engineering roles in the generative AI space

Mary Grygleski Mary Grygleski · LIVE

2:03 min

Distinguishing type definition constructs from data validation routines

Clemens Vasters Clemens Vasters · WWC 2025

Videos

See all

Related articles

See all