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Associate Principal/Forensic AI Engineer (Forensic Services practice)
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Senior ML Engineer

Ideas2IT Technologies -
Remote

Quick Apply

Job Details

Full-time | Contract
$90 - $100 an hour
13 days ago

Qualifications

AI models
TensorFlow
Software engineering
Predictive modeling analysis
Engineering development testing
Software coding
Software deployment
System design
Scalable systems
Java
System design for system development
Production systems
Machine intelligence
Bachelor's degree
Data pipeline scheduling
Model deployment
Cost reduction strategy
Developing large-scale AI models
SaaS (as product category for technical sales engineers)
Scalability
Monitoring system implementation
Model training
System deployment
Cloud Architecture Design (Architecture design skills)
Unit testing
SaaS
Model evaluation
Machine learning frameworks
Design (software development lifecycle)
MLOps

Full Job Description

Designation: ML Engineer (Sr.) Experience: 5+ years

Role:

The Technical Architect – 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, and architecting/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 technical architect or 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: $90.00 - $100.00 per hour

Work Location: Remote

Quick Apply

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AI Architect

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