ML Engineer

Designation: ML Engineer Experience: 5\+ years

Role:

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

Requirements:

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.

Pay: $80\.00 \- $100\.00 per hour

Work Location: Remote