ai research engineer

Skylabs AI

<div>About Us
At SkyLabs AI Inc., we are at the forefront of the artificial intelligence revolution. As a US-headquartered company, we conduct applied research on AI for intelligent reasoning. We specialize in complex neurosymbolic AI to solve intricate problems within software engineering. Our team is composed of world-class researchers and engineers dedicated to building the platforms and intelligent agents that will power the next generation of software. If you are passionate about building truly intelligent systems and want to make a lasting impact, join us.
The Role
We are seeking an exceptional Senior AI Research Engineer with a strong focus on training and improving LLMs across the full lifecycle&mdash;from Domain‑Adaptive Pretraining to Supervised Fine‑Tuning (SFT), RL/RLVR, and advanced post‑training techniques (e.g., reward modeling, preference optimization, RL‑VR style workflows). You will lead hands‑on training efforts, contribute to research direction, and build robust pipelines for data, training, evaluation, and deployment.
You should have in‑depth understanding of LLM internals (attention/MLP dynamics, normalization, optimization behavior, scaling laws), and modern architectures including Mixture of Experts (MoE) and other frontier design choices. You will work closely with product and platform teams to translate research ideas into performant systems.
Requirements
Key Responsibilities<ul>
<li>
Lead the team of AI Engineers/Researchers
</li><li>Train and iterate on LLMs end-to-end across DAPT/CPT, SFT, and RL-based post-training (preference optimization, reward modeling, verifiable rewards, policy optimization, and related variants).
</li><li>Design training recipes: tokenization strategy, curriculum/sampling, batch/sequence packing, optimizer + scheduler choices, stability techniques, and hyperparameter search.
</li><li>Architect and implement efficient training systems using distributed training (data/tensor/pipeline parallelism), FSDP/ZeRO, activation checkpointing, mixed precision, and throughput optimization.
</li><li>Develop and maintain LLM data pipelines: large-scale data ingestion, filtering, deduplication, contamination checks, domain balancing, safety filtering, and dataset versioning.
</li><li>Perform deep EDA and root-cause analysis for training/eval regressions (loss spikes, instability, alignment drift, memorization, toxicity, domain overfitting), and implement mitigations.
</li><li>Build evaluation and benchmarking suites: offline metrics, human preference pipelines, automatic evals, domain-specific test sets, adversarial probing, and model behavior tracking over time.
</li><li>Apply and advance model compression and efficiency: distillation from LLMs, quantization-aware considerations, pruning/sparsity (including MoE routing behavior), and inference-time optimization tradeoffs.
</li><li>Implement and operationalize LLMOps: experiment tracking, reproducible runs, model registry, training telemetry, artifact management, CI for training configs, and safe rollout strategies.
</li><li>Leverage AI coding tools (e.g., Cursor, Copilot) to accelerate development while maintaining strong engineering rigor, testability, and code review standards.
</li><li>Collaborate cross-functionally with platform, infra, and product teams to integrate trained models into agentic/production pipelines (retrieval, tool use, evaluation gates, monitoring).
</li><li>Stay current with the field and translate new ideas (architectures, post-training, data strategies, evals) into internal experiments and improvements.
</li></ul>Qualifications &amp; Skills<ul>
<li>
5+ years in ML engineering / applied research, with significant hands-on experience training large models, especially LLMs.
</li><li>Strong practical knowledge of transformer internals and modern variations (MoE, routing, load balancing, attention variants, normalization choices, long-context strategies).
</li><li>Demonstrated experience with DAPT/CPT, SFT, and RL-based post-training (e.g., reward modeling, preference datasets, verifiable rewards, policy optimization). Ability to reason about why a method works and when it fails.
</li><li>Excellent understanding of data science fundamentals: EDA, dataset design, bias/contamination, sampling strategies, and error analysis.
</li><li>Proven ability to root-cause and debug training instabilities and performance regressions (numerics, data issues, infra bottlenecks, config drift).
</li><li>Expert-level programming in Python, with strong software engineering fundamentals (data structures, algorithms, systems thinking).
</li><li>Experience with key tooling such as PyTorch, distributed training frameworks (FSDP/DeepSpeed/ZeRO), and training orchestration.
</li><li>Familiarity with LLMOps: tracking (e.g., W&amp;B/MLflow), dataset versioning, reproducibility, deployment constraints, and monitoring of model behavior.
</li><li>Comfortable building agentic pipelines around LLMs (evaluation agents, data generation/labeling loops, model-in-the-loop distillation, automated red-teaming).
</li><li>Bonus: publications, open-source contributions, or demonstrated leadership in shipping LLM training improvements.
</li></ul>Who You Are
You&rsquo;re a pragmatic researcher-engineer: you can read papers, run experiments, and also build the training and data systems that make results reproducible and shippable. You&rsquo;re meticulous about data and measurement, fast at debugging, and comfortable making tradeoffs between quality, cost, and timeline. You communicate clearly, mentor others, and thrive in an environment where you&rsquo;re trusted to drive big initiatives.
Benefits<ul>
<li>
Salaries in USD (income tax exemptions)</li>
<li>Work in Pakistan Timezone</li>
<li>Comprehensive health allowance</li>
<li>Monthly team events and activities</li>
<li>Relocation allowance (if you're moving to Islamabad)</li>
<li>Opportunity to work with top minds in the industry and academia</li>
<li>Startup culture where ideas are heard at all levels</li>
<li>Adequate annual, sick, casual and parental leaves</li></ul></div>

Senior AI Research Engineer

Tether Operations Limited

<div>Join Tether and Shape the Future of Digital Finance
At Tether, we&rsquo;re not just building products, we&rsquo;re pioneering a global financial revolution. Our cutting-edge solutions empower businesses&mdash;from exchanges and wallets to payment processors and ATMs&mdash;to seamlessly integrate reserve-backed tokens across blockchains. By harnessing the power of blockchain technology, Tether enables you to store, send, and receive digital tokens instantly, securely, and globally, all at a fraction of the cost. Transparency is the bedrock of everything we do, ensuring trust in every transaction.
Innovate with Tether
Tether Finance: Our innovative product suite features the world&rsquo;s most trusted stablecoin, USDT, relied upon by hundreds of millions worldwide, alongside pioneering digital asset tokenization services.
But that&rsquo;s just the beginning:
Tether Power: Driving sustainable growth, our energy solutions optimize excess power for Bitcoin mining using eco-friendly practices in state-of-the-art, geo-diverse facilities.
Tether Data: Fueling breakthroughs in AI and peer-to-peer technology, we reduce infrastructure costs and enhance global communications with cutting-edge solutions like KEET, our flagship app that redefines secure and private data sharing.
Tether Education: Democratizing access to top-tier digital learning, we empower individuals to thrive in the digital and gig economies, driving global growth and opportunity.
Tether Evolution: At the intersection of technology and human potential, we are pushing the boundaries of what is possible, crafting a future where innovation and human capabilities merge in powerful, unprecedented ways.
Why Join Us?
Our team is a global talent powerhouse, working remotely from every corner of the world. If you&rsquo;re passionate about making a mark in the fintech space, this is your opportunity to collaborate with some of the brightest minds, pushing boundaries and setting new standards. We&rsquo;ve grown fast, stayed lean, and secured our place as a leader in the industry.
If you have excellent English communication skills and are ready to contribute to the most innovative platform on the planet, Tether is the place for you.
Are you ready to be part of the future?
About the job
We are seeking highly motivated MSc or PhD interns to work on video generation and multimodal video foundation models. Interns will focus on one or more components of the foundation model lifecycle and are encouraged to propose creative, research-driven ideas that advance the state of the art.
You will contribute to the development and improvement of open-source video foundation models, analyze their limitations, and design scalable solutions. This is a research-focused internship with opportunities to publish at top-tier computer vision and machine learning conferences, and to work with petabyte-scale video datasets and large distributed GPU clusters with thousands of GPUs.
Responsibilities<ul><li>
Research and improve open-source video and multimodal video generation foundation models</li><li>
Focus on one or more areas such as pre-training, supervised fine-tuning, post-training, inference, architecture design, or evaluation</li><li>
Benchmark models against current state-of-the-art, identify bottlenecks, and propose novel improvements</li><li>
Work with large-scale video datasets and distributed training systems</li><li>
Collaborate with researchers and engineers on projects with clear research and publication potential</li></ul> 
Minimum Qualifications:<ul><li>
MSc or PhD candidate in Computer Science, Machine Learning, Computer Vision, or a related technical field</li><li>
Research topic or experience in image generation, video generation, or multimodal learning</li><li>
Awareness of open-source video foundation models and their current limitations</li><li>
Proficiency with PyTorch and modern deep learning workflows</li><li>
Strong analytical thinking, creativity, and collaboration skills</li><li>
Prior first-author related publications in CVPR, ICCV, ECCV, NeurIPS, or ICLR</li></ul>
Preferred Qualifications:<ul><li>
Demonstrated related work, such as research codebase or benchmarks released on GitHub or similar platforms</li><li>
Experience with large-scale or distributed training</li><li>
Hands-on experience with diffusion-based, transformer-based, or hybrid video generation models</li></ul>
Important information for candidates 
Recruitment scams have become increasingly common. To protect yourself, please keep the following in mind when applying for roles:<ul><li>
Apply only through our official channels. We do not use third-party platforms or agencies for recruitment unless clearly stated. All open roles are listed on our official careers page: https://tether.recruitee.com/</li><li>
Verify the recruiter&rsquo;s identity. All our recruiters have verified LinkedIn profiles. If you&rsquo;re unsure, you can confirm their identity by checking their profile or contacting us through our website.</li><li>
Be cautious of unusual communication methods. We do not conduct interviews over WhatsApp, Telegram, or SMS. All communication is done through official company emails and platforms.</li><li>
Double-check email addresses. All communication from us will come from emails ending in @tether.to or @tether.io</li><li>
We will never request payment or financial details. If someone asks for personal financial information or payment at any point during the hiring process, it is a scam. Please report it immediately.</li></ul>
When in doubt, feel free to reach out through our official website.</div>

AI Video Research Engineer Intern

Innodata

<div>Who we are:
Innodata (NASDAQ: INOD) is a leading data engineering company. With more than 2,000 customers and operations in 13 cities around the world, we are the AI technology solutions provider-of-choice to 4 out of 5 of the world&rsquo;s biggest technology companies, as well as leading companies across financial services, insurance, technology, law, and medicine.
By combining advanced machine learning and artificial intelligence (ML/AI) technologies, a global workforce of subject matter experts, and a high-security infrastructure, we&rsquo;re helping usher in the promise of clean and optimized digital data to all industries. Innodata offers a powerful combination of both digital data solutions and easy-to-use, high-quality platforms.
Our global workforce includes over 3,000 employees in the United States, Canada, United Kingdom, the Philippines, India, Sri Lanka, Israel and Germany. We&rsquo;re poised for a period of explosive growth over the next few years.
Position Summary:
Innodata is expanding its team of technical experts in LLM training, post-training, and evaluation systems. As an AI/ML Research Engineer, LLM Training &amp; Evaluation, you will build and optimize the technical foundations that power model improvement for foundation model builders and leading labs.
This role is ideal for someone who has hands-on experience fine-tuning and evaluating large language models (and ideally multimodal models), and who can bridge research and engineering in real-world customer environments. You will work closely with Language Data Scientists, Applied Research Scientists, data engineers, and client technical stakeholders to design and implement robust training/evaluation pipelines using both human-in-the-loop and AI-augmented methods.
The ideal candidate brings a strong computer science / machine learning engineering background, experience with modern LLM post-training workflows, and the ability to engage credibly with technical counterparts at leading AI organizations.
Who We&rsquo;re Looking For:
You have at least 2-3 years of relevant experience in machine learning engineering, applied ML systems, or research engineering, with substantial hands-on work in LLMs and multimodal foundation models. You have built, adapted, or optimized model training and evaluation pipelines, and you understand the practical realities of experimentation at scale: reproducibility, debugging, metrics quality, and iteration speed.
You are comfortable operating in ambiguous, high-complexity environments and can move from problem framing to implementation. You can collaborate effectively with both researchers and engineers, and you are credible in technical conversations with sophisticated customer stakeholders (e.g., AI researchers, ML engineers, technical product leads).
You bring a builder mindset and strong engineering judgment, while also understanding that evaluation quality and data quality are central to model improvement. You are excited to partner with human evaluation experts and language data scientists to create integrated post-training and evaluation systems.
Tell Me More:
As an AI/ML Research Engineer, LLM Training &amp; Evaluation, you will design and implement the pipelines and tooling that connect data, evaluation, and post-training. You will help customers and internal teams move from evaluation findings to measurable model improvements.
Your work may include building fine-tuning workflows (e.g., supervised fine-tuning and preference-based optimization), integrating evaluation harnesses into model development loops, improving experiment reliability and throughput, and supporting advanced evaluation scenarios such as long-context, cross-modal, and dynamic multi-turn interactions.
You will also contribute to Innodata&rsquo;s internal R&amp;D efforts, including benchmark datasets, evaluation frameworks, and reusable infrastructure for model assessment and post-training experimentation.
Responsibilities:<ul><li>
Lead or co-lead technically complex ML engineering projects from initial customer discussions through implementation and delivery</li><li>
Design, build, and improve LLM training and post-training pipelines, including data ingestion, preprocessing, fine-tuning, evaluation, and experiment tracking</li><li>
Implement and optimize evaluation systems for LLMs and multimodal models, including offline benchmarks and task-specific test harnesses</li><li>
Integrate human-in-the-loop and AI-augmented evaluation signals into model development workflows</li><li>
Build robust infrastructure and tooling for reproducible experimentation, metrics logging, and regression monitoring</li><li>
Diagnose model behavior and pipeline failures, including data issues, training instability, metric inconsistencies, and evaluation drift</li><li>
Collaborate with Language Data Scientists and Applied Research Scientists to translate evaluation frameworks into executable systems</li><li>
Work closely with customer technical stakeholders to understand goals, constraints, and success criteria; propose and implement technically sound solutions</li><li>
Contribute to internal research and platform development, including benchmark frameworks, evaluation tooling, and post-training workflow improvements</li><li>
Contribute to best practices and standards for LLM training, evaluation, and quality assurance across projects</li><li>
Mentor junior engineers and contribute to technical design reviews, documentation, and engineering rigor across the team</li></ul><ul><li>
BS/MS/PhD in Computer Science, Machine Learning, AI, Applied Mathematics, or a related quantitative technical field (MS/PhD preferred)</li><li>
2-3 years of relevant industry or research engineering experience in ML/AI systems</li><li>
Hands-on experience with LLM training / fine-tuning / post-training, including at least one of:<ul><li>
supervised fine-tuning (SFT)</li><li>
preference optimization (e.g., DPO or related methods)</li><li>
RLHF / RLAIF-style workflows</li><li>
task- or domain-adaptation of foundation models</li></ul></li><li>
Strong programming skills in Python and experience building production-quality ML code</li><li>
Experience with modern ML frameworks (e.g., PyTorch, JAX, TensorFlow) and model libraries/tooling (e.g., Hugging Face ecosystem, vLLM, distributed training stacks)</li><li>
Experience designing and implementing evaluation pipelines for LLM/ML systems, including metrics computation, dataset handling, and experiment comparisons</li><li>
Strong understanding of data pipelines and ML systems engineering, including reproducibility, observability, and debugging</li><li>
Experience with large-scale distributed ML systems and performance optimization for training/evaluation workloads (GPU/accelerator environments preferred)</li><li>
Experience with large-scale data processing and workflow orchestration in support of model training/evaluation</li><li>
Ability to collaborate directly with technical stakeholders including research scientists, ML engineers, data engineers, and customer technical leads</li><li>
Strong written and verbal communication skills, including the ability to explain complex technical tradeoffs to both technical and non-technical audiences</li></ul>
Technical Skills
ML / LLM Engineering<ul><li>
Experience training, fine-tuning, and evaluating transformer-based models</li><li>
Understanding of post-training workflows and model iteration loops</li><li>
Familiarity with inference-time considerations (latency, throughput, memory/performance tradeoffs) where relevant to evaluation or deployment</li></ul>
Evaluation &amp; Experimentation<ul><li>
Experience implementing automated evaluation pipelines and test harnesses</li><li>
Experience with experiment tracking, versioning, and reproducibility practices</li><li>
Ability to assess metric quality and ensure consistency across model comparisons</li></ul>
Software / Data Engineering<ul><li>
Proficiency in Python and strong software engineering fundamentals</li><li>
Experience with data processing pipelines, storage formats, and scalable dataset workflows</li><li>
Familiarity with CI/CD, testing, and engineering quality practices for ML systems</li></ul>
Preferred Skills<ul><li>
Experience with multimodal model training/evaluation (text + image/audio/video)</li><li>
Experience with long-context evaluation and/or model adaptation for long-context tasks</li><li>
Experience with agentic or multi-turn evaluation harnesses, tool-use simulation, or interactive environment testing</li><li>
Experience working in customer-facing technical consulting, solutions engineering, or applied research delivery</li><li>
Familiarity with LLM safety, alignment, robustness, or red-teaming evaluation approaches</li><li>
Contributions to open-source ML/LLM tooling or published technical work in relevant areas</li></ul>
How this role partners with the team
This role works closely with:<ul><li>
Language Data Scientists, who lead human evaluation design, language/data process excellence, and annotation/synthetic workflows</li><li>
Applied Research Scientists, who lead evaluation methodology, benchmarking research, and experimental design</li><li>
Data Engineers / Platform Teams, who support scalable data and infrastructure foundations</li><li>
Customer Technical Stakeholders, who rely on Innodata for expert guidance and implementation support in advanced GenAI development</li></ul>
Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission&rsquo;s guide at https://consumer.ftc.gov/articles/job-scams.
If you believe you&rsquo;ve been targeted by a recruitment scam, please report it to Innodata at verifyjoboffer@innodata.com and consider reporting it to the FTC at ReportFraud.ftc.gov.
#LI-NS1</div>

AI/ML Research Engineer, LLM Post-Training & Evaluation

Call For Referral

<div>
 
 
 Machine Learning Engineer
 
 
 
 
 Work Mode:
 
 
 Remote
 
 
 
 
 Compensation:
 
 
 $100&ndash;$120 per hour
 
 
 
 
 Work Type:
 
 
 Hourly, Independent
 
 
 
 
 Role Overview
 
 
 
 This role supports the evaluation of
 
 advanced machine learning systems
 
 in collaboration with
 
 leading AI research initiatives
 
 . It is intended for
 
 experienced machine learning engineers and researchers
 
 who can contribute to the development of
 
 high-quality evaluation suites
 
 that measure AI performance on
 
 real-world machine learning engineering tasks
 
 .
 
 
 The work focuses on translating
 
 practical ML research and engineering workflows
 
 into
 
 structured benchmarks
 
 for
 
 frontier models
 
 . This is a
 
 project-based, fully remote
 
 opportunity for professionals with
 
 strong hands-on ML research and engineering experience
 
 .
 
 
 
 Key Responsibilities
 
 
 <ul>
 <li>
 
 Design and develop
 
 detailed evaluation suites for machine learning engineering tasks
 </li>
 <li>
 
 Evaluate AI-generated solutions
 
 across model training, debugging, optimization, and experimentation workflows
 </li>
 <li>
 
 Analyze and document
 
 system behavior, performance characteristics, and limitations
 </li>
 <li>
 
 Contribute to the creation of benchmarks
 
 that reflect real-world ML engineering practices
 </li>
 <li>
 
 Maintain high standards
 
 of technical accuracy, clarity, and consistency in all deliverables
 </li>
 </ul>
 
 
 Qualifications
 
 
 <ul>
 <li>
 
 3+ years
 
 of experience in machine learning engineering or applied ML research
 </li>
 <li>
 
 Hands-on experience
 
 with model development, experimentation, and evaluation
 </li>
 <li>
 
 Background in ML research
 
 in an industry or academic setting (strongly preferred)
 </li>
 <li>
 
 Strong ability
 
 to reason about ML system design choices and technical trade-offs
 </li>
 <li>
 
 Excellent written communication skills
 
 and
 
 high attention to technical detail
 
 </li>
 </ul>
 
 
 Work &amp; Payment Details
 
 
 <ul>
 <li>
 You will operate as an
 
 independent professional
 
 </li>
 <li>
 This is a
 
 fully remote
 
 role with a
 
 flexible schedule
 
 </li>
 <li>
 Projects may be
 
 extended, shortened, or concluded
 
 based on needs and performance
 </li>
 <li>
 The work does
 
 not involve access to confidential or proprietary information
 
 from any employer, client, or institution
 </li>
 <li>
 
 Payments are made weekly
 
 via
 
 Stripe or Wise
 
 , based on services delivered
 </li>
 <li>
 Candidates requiring
 
 H1-B or STEM OPT
 
 sponsorship are
 
 not supported
 
 at this time
 </li>
 </ul>
 
 
 Application &amp; Onboarding Process
 
 
 <ul>
 <li>
 
 Upload your resume
 
 </li>
 <li>
 
 AI interview:
 
 A short
 
 15-minute
 
 conversational session to understand your background, experience, and interest in the role
 </li>
 <li>
 
 Follow-up communication
 
 within a few days with next steps and onboarding details
 </li>
 </ul>
 
 
</div>

Machine Learning Engineer (Remote)  $100 –$120/hr

CoinFantasy

CoinFantasy is seeking a skilled AI Developer/Engineer to develop and deploy innovative AI solutions within our decentralized ecosystem. The ideal candidate will bring expertise in building scalable AI systems, optimizing MLOps pipelines, and developing AI-powered applications that leverage decentralized network capabilities. This role offers a unique opportunity to work at the intersection of cutting-edge AI and blockchain technology, implementing advanced solutions including dynamic node orchestration, layer-wise model sharding, and secure peer-to-peer communication protocols. You'll be pioneering new approaches that push the boundaries of what's possible in decentralized AI applications.Job Responsibilities<ul><li>Design, train, and fine-tune deep learning models for diverse AI applications.</li><li>Develop, train, and fine-tune AI models using PyTorch/TensorFlow.</li><li>Convert research prototypes into production-grade code with optimized inference.</li><li>Implement LLM applications (RAG, prompt engineering) and multimodal systems.</li><li>Build and maintain data pipelines for training/evaluation.</li><li>Deploy models using MLOps tools (Docker, FastAPI, Kubernetes)</li><li>Monitor model performance (latency, accuracy) and debug issues in production.</li><li>Work with data scientists to operationalize research models.</li><li>Optimize GPU/CPU utilization for training and inference.</li><li>Document code, APIs, and workflows for reproducibility.</li></ul>Requirements<ul><li>Bachelor&rsquo;s or Master&rsquo;s in Computer Science, AI, Machine Learning, or related field.</li><li>Minimum 2 years of experience in AI development, machine learning, and distributed systems.</li><li>Proficiency in Python, Golang, machine learning frameworks (TensorFlow, PyTorch), and familiarity with distributed computing tools.</li><li>Prior experience in decentralised, web3, and P2P technologies is a plus.</li><li>Strong problem-solving and analytical skills, with a keen interest in deploying AI for real-world use cases.</li><li>Ability to work independently and as part of a team in a fast-paced environment.</li><li>A passion for emerging technologies and a desire to stay ahead of industry trends.</li></ul>About UsCoinFantasy is a Play to Invest platform that brings the world of investment to users through engaging games. With multiple categories of games, it aims to make investing fun, intuitive, and enjoyable for users. It features a sandbox environment in which users are exposed to the end-to-end investment journey without risking financial losses.Building on this foundation, we are now developing a groundbreaking decentralised protocol that will transform the AI landscape.Website:Benefits<ul><li>Competitive Salary</li><li>An opportunity to be part of the Core team in a fast-growing company</li><li>A fulfilling, challenging and flexible work experience</li><li>Practically unlimited professional and career growth opportunities</li></ul>Job Type: Full-timePay: ₹500,000.00 - ₹2,000,000.00 per yearBenefits:<ul><li>Leave encashment</li></ul>Work Location: In person

Generative AI Engineer

FiveRivers Technologies

<div><div><h3 class="jobSectionHeader">Job Information</h3>
<ul><div>Date Opened
<div>02/13/2026
</div></div><div>Job Type
<div>Full time
</div></div><div>Work Experience
<div>2-4 years
</div></div><div>Industry
<div>IT Services
</div></div><div>City
<div>Lahore
</div></div><div>State/Province
<div>Punjab
</div></div><div>Country
<div>Pakistan
</div></div><div>Zip/Postal Code
<div>54000
</div></div></ul></div><div><h3 class="jobSectionHeader">Job Description</h3>
<div>FiveRivers Technologies is looking for an AI/ML Engineer with 2-4 years experience, to work on cutting-edge Artificial Intelligence and Machine Learning solutions. The role will focus on designing and developing efficient and effective systems related to Generative AI, NLP, Computer Vision and Machine Learning, while collaborating with cross-functional teams. 
Responsibilities 
<ul><li>
Implementing AI solutions involving Generative AI, Agentic AI, LLMs, VLMs, Computer Vision, NLP, Machine Learning, Time-Series and Data Analytics etc. with a focus on LLM based systems. 
</li><li>
Designing system architecture, pipelines, algorithms and ML models for solving the business problems, based on client communication and research. 
</li><li>
Collaborating with cross-functional teams to integrate and deploy AI components in solutions.</li></ul><h3 class="jobSectionHeader"> 
Requirements</h3><ul><li>
2-4 years experience working on Machine Learning and Artificial Intelligence related jobs. 
</li><li>
BS or MS(preferable) degree in CS, SE, IT, EE, DS, AI or relevant field. 
</li><li>
Good Python programming, problem solving and algorithm design skills. 
</li><li>
Knowledge and experience in Generative AI, LLMs, Agentic AI and NLP, including LLM frameworks and techniques like LangChain, LangGraph, LlamaIndex, CrewAI, Google ADK, Prompt Engineering, Context Engineering, Advanced RAG, Agents and LLM Fine-tuning. 
</li><li>
Knowledge of Machine Learning and Deep Learning theory and implementation, with expertise in using libraries like SciKit-Learn, TensorFlow, PyTorch and Huggingface-transformers etc. 
</li><li>
Knowledge and experience in Computer Vision including classical image processing, CNNs, Vision-Transformer and Vision-Language Models. 
</li><li>
Experience working on LLMOps, MLOps, AI Engineering, REST APIs, CI/CD, Cloud and LLM Serving &amp; Inference. 
</li><li>
Experience working on Data Wrangling, Data Engineering and Vector DBs. 
</li><li>
Ability to do problem modeling, system design and creative algorithm design for complex business problems, while maintaining good communication with stakeholders. 
</li><li>
Knowledge and experience in Data Analysis, Statistics and Data Visualization. 
</li><li>
Familiarity with other AI areas like Time-Series, Recommendation Systems, Speech, Reinforcement Learning and Image Generation. 
</li><li>
Staying up to date with the latest AI research, advancements and tools.</li></ul><div></div><div> 
Location: DHA Business Hub, Phase VIII, Lahore</div></div></div></div>

AI/ML Engineer

Search 10000+ Ai research engineer jobs near you this March. New roles added regularly across leading industries. Apply today.

10000 ai research engineer jobs Near Me

Senior AI Research Engineer

AI Video Research Engineer Intern

AI Video Research Engineer Intern

AI/ML Research Engineer, LLM Post-Training & Evaluation

Machine Learning Engineer (Remote) $100 –$120/hr

Generative AI Engineer

AI/ML Engineer