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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # ML Engineer - **Company:** IDEAS2IT LLC. - **Location:** United States (Remote available) - **Salary:** $166,400.0 - $208,000.0 - **Contract:** Permanent contract - **Skills:** Java (Programming Language), Artificial Intelligence, Airflow, Amazon Web Services, Software Applications, Unit Testing, Microsoft Azure, Software as a Service, Cloud Computing, Continuous Integration, Data Validation, DevOps, Data Flow Control, Python (Programming Language), Machine Learning, NumPy, Tensorflow, Prometheus, Azure Machine Learning, SQL Databases, Systems Integration, TypeScript, Unstructured Data, Feature Engineering, Data Ingestion, Pytorch, Large Language Models, Grafana, Multi-Agent Systems, Prompt Engineering, Deep Learning, Model Validation, Fastapi, Pandas, Pytest, Containerization, Scikit Learn, Kubernetes, Information Technology, HuggingFace, Google Cloud Functions, Data Analytics, Xgboost, Machine Learning Operations, Code Restructuring, GPT, Serverless Computing, Docker, Programming Languages - **Published:** June 18, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=58202b65be5a1bb5 ## About the Role Do you have experience in Unit testing?, Do you have a Master's degree?, Passion for building and delivering great ML systems with a strong sense of ownership. * Minimum 5 years of experience in software/ML engineering, with at least 3-4 years focused on machine learning, deep learning, or applied AI * Strong experience in architecting and developing end-to-end ML pipelines - from data ingestion and feature engineering to model training, deployment, and monitoring * Hands-on experience with LLM fine-tuning (LoRA, QLoRA, PEFT, RLHF, instruction tuning) and building RAG-based applications * Experience designing and deploying multi-tenant ML/AI SaaS solutions * Experience designing solutions that are highly scalable and cost-optimized for inference at scale * Experience building secure ML applications including model security, data privacy, PII handling, and prompt-injection defenses * Expertise in working with structured and unstructured data at scale, including SQL and vector databases (Pinecone, Weaviate, FAISS, pgvector, Milvus, etc.) * Strong understanding of model evaluation, experiment tracking, drift detection, and continuous training Technical Competencies: * Programming languages - Python (primary), SQL; familiarity with one of Go/Java/TypeScript is a plus * Data Science & ML - NumPy, Pandas, Scikit-learn, XGBoost/LightGBM, statistical modeling, feature engineering * Deep Learning - PyTorch (preferred), TensorFlow, Hugging Face Transformers * LLM & GenAI - LLM fine-tuning (LoRA/QLoRA/PEFT), RLHF/DPO, embeddings, RAG architectures, prompt engineering, evaluation frameworks (RAGAS, DeepEval, etc.) * LLM Frameworks - LangChain, LangGraph, LlamaIndex; agentic workflows and multi-agent orchestration * MCP (Model Context Protocol) - designing and integrating MCP servers/clients for tool-augmented LLM applications * MLOps - MLflow, Kubeflow, Weights & Biases, DVC, Airflow/Prefect, model registries, CI/CD for ML, feature stores (Feast, Tecton) * Model Serving & Inference - FastAPI, BentoML, Triton Inference Server, TorchServe, vLLM, TGI, Ray Serve * Cloud (any one strong, familiarity with others) - o Azure: Azure ML, Azure OpenAI, AKS, Azure Functions, ADF, Event Hub, Cognitive Services o AWS: SageMaker, Bedrock, Lambda, EKS, Step Functions, Kinesis o GCP: Vertex AI, GKE, Cloud Functions, Dataflow, Pub/Sub * Containerization & Orchestration - Docker, Kubernetes, Helm * Observability for ML - LangSmith, Langfuse, Arize, WhyLabs, Evidently, Prometheus/Grafana * Testing - PyTest, model unit testing, data validation (Great Expectations, Pandera) Functional Competencies: * Must have very good problem-solving skills, especially in ambiguous, data-driven contexts * Must have excellent design, coding, and refactoring skills with focus on reproducibility * Must have very good communication and presentation skills, including ability to explain ML concepts to non-technical stakeholders * Should be a lateral thinker who provides simple, innovative solutions to complex ML/AI problems * Should be able to participate in multiple projects simultaneously * Must have experience deploying ML/LLM systems in production at scale * Familiarity with continuous integration and deployment practices (CI/CD) and their ML extensions (CT - Continuous Training, CM - Continuous Monitoring) * Awareness of Responsible AI practices - bias, fairness, explainability (SHAP, LIME), and AI governance Qualification: Diploma, B.E. / B.Tech / B.C.S. / M.E. / M.Tech / M.C.A / M.C.M. Specialization in Computer Science, AI/ML, Data Science, or Statistics preferred. ## Description ML Engineering will have a clear understanding of building scalable, production-grade machine learning systems and AI-powered applications. The role involves understanding business problems, translating them into ML/AI solutions/designing systems that are high-performing, secure, scalable, reproducible, and testable. It is a hands-on role involving building ML pipelines, fine-tuning models, deploying to cloud, and taking ownership of delivery by working closely with data scientists, ML engineers, and junior team members. Responsibilities include: * Minimum 1-2 years in designing ML/AI solutions as a lead ML engineer * Overseeing the development, training, evaluation, and deployment of ML and GenAI systems * Collaborating with different stakeholders, including data scientists, product teams, DevOps, and customers * Providing technical leadership and mentorship to ML engineering and data science teams * Defining ML system design standards, model governance practices, and MLOps best practices ## Related Videos - [DevOps for AI: running LLMs in production with Kubernetes and KubeFlow](https://www.wearedevelopers.com/videos/1222-devops-for-ai-running-llms-in-production-with-kubernetes-and-kubeflow) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Vectorize all the things! Using linear algebra and NumPy to make your Python code lightning fast.](https://www.wearedevelopers.com/videos/562-vectorize-all-the-things-using-linear-algebra-and-numpy-to-make-your-python-code-lightning-fast) - [ Evaluating AI models for code comprehension](https://www.wearedevelopers.com/videos/1462-evaluating-ai-models-for-code-comprehension) - [LLMOps-driven fine-tuning, evaluation, and inference with NVIDIA NIM & NeMo Microservices](https://www.wearedevelopers.com/videos/1582-llmops-driven-fine-tuning-evaluation-and-inference-with-nvidia-nim-nemo-microservices) - [Docker build without Docker](https://www.wearedevelopers.com/videos/100114-docker-build-without-docker) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Got AI ideas but no money? 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