Machine Learning Engineer

Job Description: AI/ML Engineer

Employment Type: W2 contract with Softstandard solutions
Department: AI / Data Engineering
Experience: Senior Level

About the Role

We are looking for an AI/ML Engineer to design, build, and deploy machine learning and generative AI solutions that solve real business problems. You will work with data scientists, data engineers, and product teams to take models from experimentation to production, and keep them reliable, scalable, and cost-efficient.

Key Responsibilities

• Design, train, evaluate, and optimize machine learning and deep learning models
• Build and deploy generative AI solutions using LLMs, including RAG pipelines, prompt engineering, and fine-tuning
• Develop and maintain end-to-end ML pipelines (data ingestion, feature engineering, training, deployment, monitoring)
• Productionize models as APIs and services with MLOps best practices
• Build AI agents and workflows using frameworks such as LangChain, LlamaIndex, or similar
• Monitor model performance, detect drift, and retrain models as needed
• Optimize models for latency, scalability, and cost
• Work with large datasets using SQL and distributed processing tools
• Evaluate AI outputs for quality, bias, safety, and hallucination risks
• Collaborate with cross-functional teams and document models, experiments, and results
• Stay current with new research, tools, and techniques

Required Skills and Qualifications

Programming and ML Fundamentals

• Strong Python skills, with a good understanding of data structures and clean coding practices
• Solid grounding in statistics, probability, linear algebra, and ML algorithms
• Experience with supervised, unsupervised, and deep learning techniques

ML and Deep Learning Frameworks

• PyTorch, TensorFlow, or Keras
• scikit-learn, XGBoost, or LightGBM
• Pandas, NumPy, and data visualization tools

Generative AI and LLMs

• Hands-on experience with LLMs (OpenAI, Anthropic Claude, Llama, Mistral, or similar)
• RAG architectures, embeddings, and vector databases (Pinecone, FAISS, Weaviate, Chroma, or pgvector)
• Prompt engineering, fine-tuning (LoRA/PEFT), and evaluation methods
• Experience with Hugging Face Transformers, LangChain, or LlamaIndex

MLOps and Deployment

• MLflow, Kubeflow, or similar tools for experiment tracking and pipelines
• Docker and Kubernetes
• Model serving with FastAPI, Flask, or TorchServe
• CI/CD for ML workflows (GitHub Actions, Jenkins, Azure DevOps)

Cloud and Data

• Experience with AWS (SageMaker, Bedrock), Azure (Azure ML, Azure OpenAI), or GCP (Vertex AI)
• Strong SQL skills and knowledge of data warehouses or lakehouses (Snowflake, Databricks, BigQuery)
• Familiarity with Spark or other big data tools

Other

• Understanding of responsible AI, data privacy, and model governance
• Strong problem-solving and analytical skils