Coherent Solutions, Inc.
Our client is a U.S.-based company specializing in software solutions for the roofing industry. Its flagship product enables homeowners and contractors to receive instant roofing quotes through an AI-driven platform. The company is dedicated to modernizing the roofing process through innovation, automation, and seamless user experiences.
Project Description The project focuses on designing and delivering an intelligent voice-interaction system. The solution bridges the gap between digital lead generation and human sales engagement by enabling natural, AI-driven phone conversations with potential clients. The assistant will answer inquiries, schedule appointments automatically, and integrate seamlessly into a well-established SaaS platform for roofing contractors that automates quoting, lead management, and sales operations.
Technologies
GenAI
LLMs
LangChain/LangGraph
Whisper/Amazon Transcribe
Amazon Polly
Python (FastAPI)
PyTorch and/or TensorFlow
GitHub Actions
Docker
Terraform
Grafana
ELK
DataDog
OpenTelemetry
What You'll Do
Lead the design, development, and deployment of scalable ML services;
Architect and implement AI/ML solutions centered around LLMs, leveraging technologies like RAG, summarization, sentiment analysis, and reasoning;
Optimize ML workflows for performance and cost efficiency on cloud platforms (AWS, Azure, or GCP), utilizing infrastructure-as-code and monitoring tools;
Work closely with data scientists, analysts, and business stakeholders to translate requirements into technical solutions;
Mentor and guide junior engineers, fostering a culture of best practices and knowledge sharing;
Develop and maintain documentation, ML models, and technical standards;
Monitor, troubleshoot, and resolve issues in production for ML pipelines and environments;
Stay up-to-date with emerging trends and technologies in AI/ML field;
Job Requirements
Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or a related field;
8+ years of experience in ML engineering, with at least 1 year of hands‑on experience working with LLMs;
Strong programming skills (Python), including production-level code development and API/service design;
Hands‑on experience with AWS (S3, EC2, Lambda, ECS, ECR, SageMaker, Bedrock, IAM, CloudWatch) and/or Azure (Blob Storage, Data Factory, Azure ML, Azure OpenAI);
Strong proficiency in PyTorch, TensorFlow, and experience with MLflow for experiment tracking and model registry;
Experience with LangChain/LangGraph, LlamaIndex and/or similar frameworks;
Familiarity with Vector Databases such as Qdrant, PosgreSQL (pgvector), Pinecone, Weaviate, or FAISS;
Experience with Git, CI/CD practices for ML and data pipelines;
Familiarity with data governance, security, and compliance in cloud environments;
Experience with Object detection models (YOLO, SDD) and Segmentation models (SAM2) is nice to have;
Level of English - from B2 (spoken/written);
What Do We Offer The global benefits package includes:
Technical and non-technical training for professional and personal growth;
Internal conferences and meetups to learn from industry experts;
Support and mentorship from an experienced employee to help you professional grow and development;
Internal startup incubator;
Health insurance;
Sports activities to promote a healthy lifestyle;
Flexible work options, including remote and hybrid opportunities;
Referral program for bringing in new talent;
Work anniversary program and additional vacation days.
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Project Description The project focuses on designing and delivering an intelligent voice-interaction system. The solution bridges the gap between digital lead generation and human sales engagement by enabling natural, AI-driven phone conversations with potential clients. The assistant will answer inquiries, schedule appointments automatically, and integrate seamlessly into a well-established SaaS platform for roofing contractors that automates quoting, lead management, and sales operations.
Technologies
GenAI
LLMs
LangChain/LangGraph
Whisper/Amazon Transcribe
Amazon Polly
Python (FastAPI)
PyTorch and/or TensorFlow
GitHub Actions
Docker
Terraform
Grafana
ELK
DataDog
OpenTelemetry
What You'll Do
Lead the design, development, and deployment of scalable ML services;
Architect and implement AI/ML solutions centered around LLMs, leveraging technologies like RAG, summarization, sentiment analysis, and reasoning;
Optimize ML workflows for performance and cost efficiency on cloud platforms (AWS, Azure, or GCP), utilizing infrastructure-as-code and monitoring tools;
Work closely with data scientists, analysts, and business stakeholders to translate requirements into technical solutions;
Mentor and guide junior engineers, fostering a culture of best practices and knowledge sharing;
Develop and maintain documentation, ML models, and technical standards;
Monitor, troubleshoot, and resolve issues in production for ML pipelines and environments;
Stay up-to-date with emerging trends and technologies in AI/ML field;
Job Requirements
Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or a related field;
8+ years of experience in ML engineering, with at least 1 year of hands‑on experience working with LLMs;
Strong programming skills (Python), including production-level code development and API/service design;
Hands‑on experience with AWS (S3, EC2, Lambda, ECS, ECR, SageMaker, Bedrock, IAM, CloudWatch) and/or Azure (Blob Storage, Data Factory, Azure ML, Azure OpenAI);
Strong proficiency in PyTorch, TensorFlow, and experience with MLflow for experiment tracking and model registry;
Experience with LangChain/LangGraph, LlamaIndex and/or similar frameworks;
Familiarity with Vector Databases such as Qdrant, PosgreSQL (pgvector), Pinecone, Weaviate, or FAISS;
Experience with Git, CI/CD practices for ML and data pipelines;
Familiarity with data governance, security, and compliance in cloud environments;
Experience with Object detection models (YOLO, SDD) and Segmentation models (SAM2) is nice to have;
Level of English - from B2 (spoken/written);
What Do We Offer The global benefits package includes:
Technical and non-technical training for professional and personal growth;
Internal conferences and meetups to learn from industry experts;
Support and mentorship from an experienced employee to help you professional grow and development;
Internal startup incubator;
Health insurance;
Sports activities to promote a healthy lifestyle;
Flexible work options, including remote and hybrid opportunities;
Referral program for bringing in new talent;
Work anniversary program and additional vacation days.
#J-18808-Ljbffr